CN113330497A - Automatic driving method and device based on intelligent traffic system and intelligent traffic system - Google Patents
Automatic driving method and device based on intelligent traffic system and intelligent traffic system Download PDFInfo
- Publication number
- CN113330497A CN113330497A CN202080000912.2A CN202080000912A CN113330497A CN 113330497 A CN113330497 A CN 113330497A CN 202080000912 A CN202080000912 A CN 202080000912A CN 113330497 A CN113330497 A CN 113330497A
- Authority
- CN
- China
- Prior art keywords
- road
- vehicle
- automatic driving
- information
- scheme
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 71
- 238000012806 monitoring device Methods 0.000 claims abstract description 48
- 230000005540 biological transmission Effects 0.000 claims abstract description 13
- 238000004458 analytical method Methods 0.000 claims description 8
- 238000012544 monitoring process Methods 0.000 claims description 8
- 239000000463 material Substances 0.000 claims description 4
- 238000013459 approach Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 14
- 230000001133 acceleration Effects 0.000 description 8
- 230000006399 behavior Effects 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 8
- 238000011156 evaluation Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 7
- 230000008901 benefit Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000005265 energy consumption Methods 0.000 description 4
- 238000007726 management method Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000013473 artificial intelligence Methods 0.000 description 3
- 230000006378 damage Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000005611 electricity Effects 0.000 description 3
- 239000000446 fuel Substances 0.000 description 3
- 241001465754 Metazoa Species 0.000 description 2
- 206010039203 Road traffic accident Diseases 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 230000001939 inductive effect Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 208000028399 Critical Illness Diseases 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000003190 augmentative effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 201000003152 motion sickness Diseases 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000035484 reaction time Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Human Computer Interaction (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Traffic Control Systems (AREA)
Abstract
The automatic driving method comprises the steps of obtaining road information, road condition information and vehicle information, building a road traffic model, and intelligently analyzing the execution of the whole automatic driving scheme of the road vehicle. The automatic driving device comprises a control component (21) and a wireless transmission component (22). The intelligent transportation system includes: a road monitoring device (31), a signal transceiver (32) and a server (33). The road information, the road condition information and the vehicle information are comprehensively acquired by a road monitoring device, a vehicle-mounted monitoring device or other monitoring devices, and an integral automatic driving scheme is provided for vehicles on the road, so that the problems of incomplete information acquisition, mutual conflict of decision making of the vehicles and the like are effectively avoided, the driving safety and the driving efficiency of the vehicles are greatly improved, and the function of adding manual driving into automatic driving of the vehicles with low cost can be conveniently realized.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to an automatic driving method and device based on an intelligent traffic system and the intelligent traffic system.
Background
With the continuous development of science and technology, automatic driving vehicles have appeared, and the development of intelligent transportation is greatly promoted. The automatic driving vehicle mainly depends on the cooperation of artificial intelligence, visual calculation, radar, a monitoring device and a global positioning system to realize the automatic driving of the vehicle, and the drivable area of the vehicle is determined by data returned by a sensor. The content of environment perception can be divided into two parts, namely road information and road condition information, wherein the target information can provide targets of all areas around the vehicle for the automatic driving vehicle, so that the automatic driving vehicle can make correct reactions, such as overtaking, decelerating, following and the like, to realize automatic control, and the road information provides a drivable road for the vehicle to plan a path and bring the vehicle to a specified area position. However, in a region with a view or a sensing blind area, the autonomous vehicle may not make an appropriate and accurate decision because the surrounding situation is not sensed in time.
An Intelligent Transportation System (ITS) refers to a comprehensive management System that effectively integrates and applies advanced information technology, data communication transmission technology, electronic sensing technology, electronic control technology, computer processing technology and the like to the whole Transportation management System on a perfect infrastructure, thereby establishing a real-time, accurate and efficient comprehensive management System that can play a role in a large range and all-around. The intelligent traffic system is used as a development direction of a future traffic system, and has important significance in the aspects of reducing the pressure of the traffic system, ensuring the driving safety of vehicles, improving the transportation efficiency of the vehicles and the like. Therefore, a method for organically combining the automatic driving technology with the intelligent transportation is urgently needed to better realize the safety and the high efficiency of the automatic driving of the vehicle.
Disclosure of Invention
The main purposes of the invention are: the automatic driving method and device based on the intelligent traffic system and the intelligent traffic system are provided, road information, road condition information and vehicle information are obtained through a road monitoring device and a vehicle-mounted monitoring device, a road traffic model is built, the whole automatic driving scheme of the road vehicle is intelligently analyzed and executed, the problems that the information is not comprehensively obtained, the decisions of the vehicles conflict with each other and the like are effectively avoided, and the driving safety of the vehicles and the driving efficiency of the vehicles are greatly improved.
In order to achieve the above object, the present invention provides an automatic driving method based on an intelligent transportation system, the method comprising:
the intelligent traffic system acquires road information and road condition information;
the system acquires information of vehicles running on a road;
the system establishes a road traffic model and intelligently analyzes the whole automatic driving scheme of the road vehicle;
the system transmits the plan to an autonomous vehicle, which executes autonomous driving according to the plan.
In the automatic driving method, the path for acquiring the traffic information by the intelligent transportation system includes: at least one approach of a road monitoring device, a vehicle-mounted monitoring device or a high-altitude monitoring device.
The automatic driving method as described above, the road information including: the number of lanes, lane width, curvature radius, gradient, road material, entrance and exit, traffic lights, road junctions, connecting roads, road environment, and road surface condition.
The automatic driving method as described above, further comprising obtaining information about the manually driven vehicle on the road for predicting a driving expectation behavior of the manually driven vehicle.
The automated driving method as described above, the method further comprising transmitting the road vehicle overall automated driving scheme to a human-driven vehicle for prompting a driver of a driving situation of the automated driving vehicle or guiding the driver to drive the vehicle.
The automatic driving method as described above, further comprising: under the same road traffic model, the overall automatic driving schemes of different road vehicles are evaluated and compared, and the optimal overall automatic driving scheme of the road vehicles is preferentially provided.
According to the automatic driving method, when the road traffic model changes, intelligent analysis is carried out again, a new road vehicle overall automatic driving scheme is obtained and is transmitted to the automatic driving vehicle to be executed.
According to the automatic driving method, the method further comprises selecting a proper coverage area to establish the road traffic model according to the actual conditions of the road information and the road condition information.
The automatic driving method as described above, further comprising converting and/or integrating the acquired information of different data sources/data structures/data standards/data formats/data descriptions.
In the automatic driving method, the establishing of the road traffic model is to obtain the road traffic model suitable for the actual situation by selecting the existing road traffic model with high similarity to the actual situation and directly applying or correspondingly modifying the existing road traffic model according to the actual information.
The invention also provides an automatic driving device of the vehicle, which comprises a control component and a wireless transmission component; the wireless transmission assembly is used for wirelessly transmitting monitoring data of the vehicle-mounted monitoring device and receiving an automatic driving scheme; the control component is used for controlling the vehicle to run according to the automatic driving scheme.
The present invention also provides an intelligent transportation system, the system comprising: a road monitoring device, a signal transceiver and a server,
the road monitoring device is used for acquiring road information and road condition information;
the signal transceiver is used for transceiving signals;
the server acquires road information, road condition information and vehicle information running on the road; establishing a road traffic model through the acquired information, and intelligently analyzing an overall automatic driving scheme of the road vehicle; and transmitting the scheme to an automatic driving vehicle through the signal transceiver, and executing automatic driving by the automatic driving vehicle according to the scheme.
The invention discloses an automatic driving method and device based on an intelligent traffic system and the intelligent traffic system. The autopilot device includes a control assembly and a wireless transmission assembly. The intelligent transportation system includes:
road monitoring devices, signal transceiver and server. According to the automatic driving method and device based on the intelligent traffic system and the intelligent traffic system, the road information, the road condition information and the vehicle information are comprehensively acquired through the road monitoring device, the vehicle-mounted monitoring device or other monitoring devices, and an integral automatic driving scheme is provided for vehicles on the road, so that the problems of incomplete information acquisition, mutual conflict of vehicle decisions and the like are effectively avoided, the driving safety and the driving efficiency of the vehicles are greatly improved, and the function of adding manually driven vehicles into automatic driving can be conveniently realized at low cost.
Drawings
Fig. 1 is a flowchart of a method for automatic driving based on an intelligent transportation system according to a first embodiment of the present invention.
Fig. 2 is a schematic diagram of an automatic driving apparatus of a vehicle according to a second embodiment of the present invention.
Fig. 3 is a schematic diagram of an intelligent transportation system according to a third embodiment of the present invention.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the intended purpose, the following detailed description of the embodiments of the present invention is provided in conjunction with the accompanying drawings and examples.
A first embodiment of the present invention is described with reference to fig. 1. Fig. 1 is a flowchart of a method for automatic driving based on an intelligent transportation system according to a first embodiment of the present invention. As shown in the figure, the automatic driving method based on the intelligent transportation system of the invention comprises the following steps:
step 1: the intelligent traffic system acquires road information and road condition information.
The road information and the road condition information are important basis for establishing a road traffic model and also important basis for analyzing and obtaining an automatic driving scheme. Therefore, the fact that complete and comprehensive road information and road condition information are obtained is the guarantee of the accuracy and the safety of the automatic driving scheme. The road information includes: the number of lanes, the width of lanes, the radius of curvature, the gradient, the material of the road, the entrance and exit, the traffic lights, the road junctions, the connecting roads, the road environment, the road surface conditions (including friction, bearing, height limit, speed limit, etc.), and other information related to the road. The road condition information includes: the traffic information includes information related to navigation such as a traffic volume, a vehicle position, a vehicle speed, a vehicle acceleration, a vehicle object, obstacle/pedestrian information, traffic signal information, a road surface damage condition, a traffic accident, and the like.
The intelligent traffic system can acquire road information and road condition information through the road monitoring device and the vehicle-mounted monitoring device, and can also acquire the road information and the road condition information through other monitoring devices such as an overhead monitoring device and the like. The road monitoring device may include a camera, a radar, an inductive sensor, an infrared detection device, a pressure/optical/ultrasonic sensor of the road or the road surface, etc., and a plurality of monitoring devices may be provided at appropriate positions of the road for acquiring such information. In addition, existing vehicles, whether autonomous or manually driven, also typically include some vehicle-mounted monitoring devices, such as vehicle-mounted cameras, vehicle-mounted radars, speedometers, and the like. Road information and road condition information can also be acquired through high-altitude monitoring devices such as satellites/airplanes/unmanned aerial vehicles/high-altitude balloons and the like. Related information can also be obtained by monitoring the hardware of the internet of things, the radio frequency card, the ECT equipment and the like of the vehicle. Finally, the related information of the road condition can be obtained by monitoring the range of the road periphery which may affect the road condition, such as roadside pedestrians/animals/vehicles/buildings/stations and the like. The advantage of obtaining information in multiple ways is that the information is more comprehensive, and the loss caused by a single information source is avoided. For example: the existing automatic driving technology usually relies on a monitoring device of a vehicle to acquire road condition information, and the road condition information is easily shielded, so that some information cannot be acquired.
Step 2: the system obtains information of vehicles traveling on a road.
The vehicle information is also an important basis for establishing a road traffic model and analyzing to obtain an automatic driving scheme. The vehicle information may include: vehicle type, model, license plate number, length/width/height/mass/braking distance/tire condition/power condition/electric quantity/oil quantity etc. parameters of the vehicle, vehicle destination, number of passengers, etc.
The approach of acquiring the vehicle information may include: the method comprises the steps of receiving vehicle information sent by a vehicle actively, information replied by the vehicle after a system inquires about the vehicle, obtaining the information through monitoring by a road monitoring device or other devices, obtaining the information through inquiry after identifying the vehicle model or license plate number, and the like.
In the present invention, since the acquired information comes from different sources, there may be a situation where the data structure, data standard, data format, data description, etc. of the acquired information are different, and in this situation, for the purpose of fluency and high efficiency of information usage, it is necessary to convert and/or integrate information of different sources and types. The conversion and/or integration of information data can be realized by methods such as a video recognition technology, an audio recognition technology, a vehicle/license plate recognition technology, a three-dimensional/four-dimensional modeling technology, a virtual reality technology, an augmented reality technology, translation of different languages and the like.
And step 3: the system establishes a road traffic model and intelligently analyzes the whole automatic driving scheme of the road vehicle.
The system uses the obtained information to build a road traffic model, which may include: roads, vehicles, obstacles, pedestrians, coverage, time of coverage, weather conditions, special circumstances and other factors related to road traffic. The method specifically comprises the following steps: road width, traffic flow, vehicle position/model/speed/acceleration/braking distance, obstacle position/size, speed/direction/intent/possible behavior of pedestrians, etc., weather conditions such as visibility/rain/snow/ice on the road, special conditions such as day-night discrepancies/traffic tide laws/traffic control or restriction planning/vehicle weight/time-first/time-limited arrival of special tasks and avoidance/out-of-road coverage of other vehicles, etc., and other content affecting road traffic including various vehicles/objects/people outside the road, etc. The coverage range of the road traffic model can be set according to actual conditions, and the coverage range can be a small section of road, a complete road, a plurality of roads, an area range, an urban range and a wider range. The more abundant and real the information obtained by the system, the more parameters contained in the road traffic model, the closer the established road traffic model is to the reality, and the more perfect the whole automatic driving scheme of the road vehicle obtained by analyzing according to the model. After the model is built, the information in the model range according to the reality and the integrity comprises: the space information/time information/object information/other information such as traffic control or restriction or traffic lights and the like are calculated and analyzed to obtain the overall automatic driving scheme of the road vehicle.
The road traffic model can be established by newly establishing the road traffic model according to the acquired information, or by selecting the existing road traffic model with high similarity to the actual situation according to the acquired information, and directly applying or correspondingly modifying the existing road traffic model according to the actual information to obtain the road traffic model suitable for the actual situation. The judgment standard of the similarity can be set in advance, can be obtained according to big data analysis/artificial intelligence deep learning, and can be continuously optimized and perfected in the actual use process.
In the present invention, the automated driving to be achieved is an overall automated driving scheme for vehicles on a road. Compared with the existing automatic driving only aiming at a single vehicle, the method has great advantages of calculating and analyzing the automatic driving scheme on the whole. First, for the entire automatic driving scheme of the road vehicles, each automatic driving vehicle on the road is executed according to the entire automatic driving scheme, and then, the expected running tracks corresponding to the automatic driving vehicles are known, and only the running tracks of the manual driving vehicles need to be predicted. The automatic driving scheme of a single vehicle needs to predict the driving track of each vehicle. In addition, the automatic driving scheme for the whole road vehicle is considered from the whole road vehicle when calculating and analyzing the planning scheme, so that the collision of the running tracks among the vehicles in the scheme is avoided, and the safety and the efficiency of the whole road vehicle are maximized. The automatic driving scheme of a single vehicle only considers the driving efficiency and the safety of the vehicle, so that the automatic driving schemes of different vehicles can influence each other, and the driving efficiency and the safety are reduced. Therefore, the automatic driving method of the invention is superior to the existing automatic driving method in efficiency and safety. Meanwhile, the automatic driving method of the invention can plan the optimal driving route for the respective automatically driven vehicle according to the vehicle destination and the real-time road condition.
After the information is acquired and the road traffic model is established, the overall automatic driving schemes of the road vehicles obtained through analysis and calculation may be multiple, and different overall automatic driving schemes of the road vehicles may have advantages. The automatic driving method of the present invention further includes: under the same road traffic model, the overall automatic driving schemes of different road vehicles are evaluated and compared, and the optimal overall automatic driving scheme of the road vehicles is preferentially provided.
The specific method comprises the following steps: under the same road traffic model, corresponding grade/score is set for each vehicle in the road vehicle integral automatic driving scheme according to the result of target evaluation such as safety, driving efficiency, comfort, energy consumption and the like, and sets target weights for safety/driving efficiency/comfort/energy consumption and other targets, and sets the vehicle weight of each vehicle according to the vehicle type/number of passengers/vehicle value of each vehicle, so that it is possible to estimate the rank/score and target weight according to the vehicle target in the road vehicle overall automatic driving scheme, and the vehicle weight of each vehicle, calculating the comprehensive grade/score of the whole automatic driving scheme of the road vehicle, so as to comprehensively compare and sequence the whole automatic driving schemes of a plurality of road vehicles under the same road traffic model.
The optimal scheme is a scheme that the vehicle safety, driving efficiency, comfort, energy consumption and the like are comprehensively evaluated, and the highest comprehensive score is obtained by combining each target weight and the vehicle weight. By setting different levels/scores/weights for different targets, various optimal solutions can be achieved. For example: for rigid targets that must be achieved, such as ambulance/fire truck to arrive at a given location within 30 minutes, the level may be set to top priority, so if the target fails to be achieved, the overall level/score of the solution must be lower than the solution achieved for the target. The rigid target may further include: military, police, medical, emergency, safety, and other critical goals. It is also possible to classify/group vehicles/pedestrians/goods according to the vehicle type, passenger type, goods type, destination, distance, route/route model, etc., and then set their weights by classification/grouping. The setting and optimization of the grade/score/weight can be based on the research result with professionalism/authority, the data obtained by big data analysis/information reformation, the data obtained by artificial intelligence deep learning, the new data obtained by statistical analysis in the using process of the original data, or the combination of the above methods. The setting and optimization of the grade/score/weight can be manual setting, can also be artificial intelligent automatic setting, and can also be semi-manual semi-automatic setting.
In the present invention, the elements included in the security object may include: accident possibility/accident type/possible injury person/possible death person/possible economic loss/possible outcome/possible impact etc. The elements included in the driving efficiency target may include: travel time/travel speed/mileage/trip completion/target achievement rate/timeliness evaluation, and the like. Elements included in the comfort goal may include: running speed/running acceleration and deceleration/sharp turning times/sharp braking times/bumping degree and the like. The elements included in the energy consumption target may include: vehicle fuel consumption (electricity consumption)/total fuel consumption (electricity consumption)/unit trip fuel consumption (electricity consumption) and the like. The vehicle weight can be set according to the vehicle type, the passenger group, the vehicle carrying goods and the like. The vehicle types may include: passenger/freight/large/medium/small/special purpose/luxury vehicles, etc. The passenger population may include: children, the old, pregnant women, carsickness patients, patients and the like. The vehicle carrying article may include: dangerous goods, fragile goods, volatile goods and the like.
For example: considering the braking and power conditions of the vehicle, the braking comprises the road surface and the tire, the braking reaction time comprises the time for acquiring signals/the time for establishing a model/the time for analyzing/the time for deciding/the time for transmitting a scheme/the time for executing the scheme, the safety braking distance is comprehensively obtained, and then the distance between the current vehicle and the front vehicle is compared with the safety braking distance to obtain the corresponding grade/score.
The level/score and weighting factor settings may be adjusted according to different practical situations, for example, a special type of vehicle, such as a police car performing a mission, is generally weighted higher than a private car, but a private car with a critically ill patient may be weighted higher than a general special type of vehicle. For another example: under normal weather conditions, the distance between the vehicle and the front vehicle is 20 meters, which does not affect the level/score of the safety aspect, but the distance between the vehicle and the front vehicle is 20 meters in rainy days, which may cause the level/score of the safety aspect to be reduced.
The road vehicle integral automatic driving scheme is obtained by intelligently analyzing a road traffic model, the input elements are actual information based on the establishment of the road traffic model, and the input elements can comprise: road information such as lane number/lane width/curvature radius/gradient/road material/entrance/traffic lights/crossing/connecting road/road environment/road surface condition/friction/load bearing/height limit/speed limit/climate condition/visibility condition, road condition information such as traffic flow/vehicle position/vehicle speed/vehicle acceleration/vehicle object/navigation-related information/obstacle information/pedestrian information/traffic signal light information/road surface damage condition/traffic accident condition, vehicle information such as vehicle type/model/license plate number/length/width/height/mass/braking distance/tire condition/power condition/electric quantity/oil quantity/other vehicle information Parameters/vehicle destination/number of occupants, and other information related to the overall automated driving scheme of the road vehicle. The output elements of the road vehicle overall automatic driving scheme may include: the running route/running track/speed/acceleration/timing of steering/steering amplitude/horn control/lamp control/distance control (including lateral and longitudinal distances) to other vehicles or pedestrians or obstacles/brake control/prompt to the person in the vehicle/prompt to the person driving the vehicle manually and running advice/auxiliary driving control to the autonomous vehicle, and the like.
The intelligent analysis of the road vehicle integral automatic driving scheme through the road traffic model is a complex process and comprises a large amount of calculation, but the automatic driving is a behavior with high real-time requirement, so the analysis of the road vehicle integral automatic driving scheme is required to be completed within a limited short time, and the automatic driving method is required to be balanced in the aspects of real-time performance and the optimal solution of the scheme. The evaluation of real-time performance can be added in the evaluation and comparison of the whole automatic driving scheme of the road vehicle, and for the whole automatic driving scheme of a certain road vehicle, the evaluation in the aspects of safety, driving efficiency, comfort and the like and the evaluation in the aspect of real-time performance are considered, because the too low real-time performance inevitably influences the driving efficiency of automatic driving and probably influences the safety, thereby reducing the comprehensive evaluation of the scheme.
In the present invention, the method for reducing the amount of calculation for analyzing the entire automatic driving scheme of the road vehicle to improve the real-time performance may be: and selecting a proper coverage area to establish a road traffic model according to the actual conditions of the road information and the road condition information. By properly reducing the size of the road traffic model, the route length of the model is reduced, the number of vehicles and obstacles in the model is reduced, the calculation amount can be greatly reduced, and the real-time performance of the scheme is greatly improved. The corresponding grade/score/weight can be set according to the evaluation result of the safety, the driving efficiency, the comfort and the like of the vehicle under the same or similar road traffic model, and the storage/recording is carried out, so that the vehicle can be directly used or related settings can be used for reference under the same/similar conditions, and the similar or partially similar vehicles can be subjected to centralized processing, the whole packaging calculation, the calculation amount is reduced, and the real-time performance is improved.
The method for improving the real-time performance can also be as follows: defining vehicles/obstacles in a road traffic model to a limited number of states, for example vehicle states may include: acceleration, deceleration, parking, left turning, right turning, lane changing, overtaking, avoiding and the like. By limiting the finite state of each element in the road traffic model, the computation amount can be reduced, and the real-time performance is improved.
And 4, step 4: the system transmits the plan to an autonomous vehicle, which executes autonomous driving according to the plan.
After an optimal road vehicle overall automatic driving scheme is intelligently analyzed and evaluated through a road traffic model, the scheme is transmitted to all automatic driving vehicles in the road traffic model, and the automatic driving vehicles execute the scheme and automatically drive according to respective driving tracks, real-time speeds and real-time accelerations.
According to the automatic driving method based on the intelligent traffic system, the road monitoring device, the vehicle-mounted monitoring device or other monitoring devices are used for comprehensively acquiring the road information, the road condition information and the vehicle information, and an overall automatic driving scheme is provided for the vehicles on the road, so that the problems that the information acquisition is not comprehensive, the decisions of the vehicles conflict with each other and the like are effectively avoided, and the driving safety of the vehicles and the driving efficiency of the vehicles are greatly improved.
In the invention, in order to further improve the safety/efficiency/comfort of the whole automatic driving scheme of the road vehicle, reduce the calculation amount and improve the real-time performance, the automatic driving method based on the intelligent transportation system can further comprise the step of acquiring the relevant information of the manually driven vehicle on the road for predicting the expected running behavior of the manually driven vehicle. The travel expectation behavior of the manually-driven vehicle refers to the travel behavior that the manually-driven vehicle may take, and the expectation probability of the travel behavior. The information related to the manually driven vehicle may include: vehicle condition, driver, driving style, driving level, driving preferences, special habits, destination, route planning, etc. For example: if the manually driven vehicle is braked suddenly frequently, more safe driving distance needs to be kept for the vehicle when the whole automatic driving scheme of the road vehicle is adopted, and the like. The acquisition of the relevant information of the manually driven vehicle can be realized by collecting and storing the relevant information through a self-built database, or by acquiring a database of a traffic management department or other relevant organizations according to the information of the license plate number and/or the driver after identifying the license plate number and/or the face of the driver, or by analyzing the relevant information continuously acquired in the using process of the method.
The automatic driving method based on the intelligent transportation system can also comprise the step of transmitting the whole automatic driving scheme of the road vehicle to a manual driving vehicle for prompting a driver to automatically drive the driving condition of the vehicle or guiding the driver to drive the vehicle. The driver of the manual driving vehicle can know the expected driving track of the automatic driving vehicle on the road in advance, and can also obtain the guidance of the automatic driving method based on the intelligent traffic system, so as to guide the driver to adopt the driving behaviors of correct deceleration, acceleration, doubling, parking and the like under the condition, thereby greatly reducing the probability of accidents, improving the driving efficiency and improving the safety.
In the present invention, if the autonomous vehicle does not yet hit the autonomous driving level of the actively driven vehicle, only the assist driving level is reached. The automatic driving method based on the intelligent transportation system can transmit the whole automatic driving scheme of the road vehicle to the vehicles with auxiliary driving levels, so that the auxiliary driving system of the vehicle can remind/suggest/alarm/actively control the vehicle to run according to the vehicle track of the scheme.
The method further comprises when the road traffic model changes, for example: and when the manually driven vehicle is not driven/braked suddenly/accelerated suddenly according to the indication, or the road vehicle is in an accident/fault suddenly, or a new vehicle/pedestrian/obstacle is added, or the road environment/climate environment is changed, the road traffic model is updated according to the information acquired in real time, the intelligent analysis is carried out again, the whole automatic driving scheme of the new road vehicle is obtained, and the scheme is transmitted to the automatically driven vehicle for execution.
The automatic driving method based on the intelligent traffic system can also be suitable for non-road areas such as grasslands, deserts, wastelands and the like. The method comprises the steps of obtaining terrain and ground information, vehicle information and information of other vehicles/obstacles/pedestrians or animals on the ground through a vehicle-mounted monitoring device, a high-altitude monitoring device and the like, establishing a model with a certain coverage area, and obtaining an automatic driving scheme through intelligent analysis. Compared with the road, the non-road area has the advantages that the vehicles can not travel along the road any more, the range of possible vehicle traveling is enlarged, and meanwhile, the number of vehicles in the scheme is reduced because the density of the vehicles in the non-road area is far less than that of the vehicles on the road. The automatic driving method based on the intelligent traffic system can be correspondingly optimized according to the characteristics of the non-road area, and the efficiency and the safety of the non-road area are improved.
A second embodiment of the present invention is seen in figure 2. Fig. 2 is a schematic diagram of an automatic driving apparatus of a vehicle according to a second embodiment of the present invention. As shown, the autopilot device includes a control assembly 21 and a wireless transmission assembly 22; the wireless transmission component 22 is used for wirelessly transmitting monitoring data of the vehicle-mounted monitoring device and receiving an automatic driving scheme; the control module 21 is used to control the vehicle to run according to the autopilot profile. The control module 21 is connected with a control system of the vehicle, and can transmit the road vehicle overall automatic driving scheme received by the wireless transmission module 22 to the control module 21. The control module 21 transmits the road-vehicle overall automatic driving scheme to the control system of the vehicle, and the control system of the vehicle controls the vehicle to travel according to the travel track of the vehicle of the road-vehicle overall automatic driving scheme. The control module 21 may also include one or more control devices for controlling the vehicle to travel according to the road-vehicle overall automatic driving scheme, and after the control module 21 receives the road-vehicle overall automatic driving scheme received by the wireless transmission module 22, each control device controls the vehicle to travel according to the vehicle travel track of the road-vehicle overall automatic driving scheme. In the present embodiment, the required control components are different for different vehicles. For example: the control system of the automobile possibly with high intelligence degree can completely control the automobile to run according to the whole automatic driving scheme of the road vehicle or only needs the control device to add partial control functions to the control system of the automobile, the traditional automobile possibly with low intelligence degree has low automation degree of the automobile, and a large number of control devices are needed to be modified to adapt to unmanned automatic driving control, such as automatic control of a plurality of components, such as a steering wheel, an accelerator, a brake, a transmission, a hand brake, light, a horn, automobile condition monitoring and the like. The automatic control devices of different components are different, the control devices of different levels of automatic driving are also different, for example, the control device required by unmanned driving requires the most complete functions, and the requirement and the function of the control device required by auxiliary driving are lower than those required by unmanned driving.
The automatic driving device of the vehicle can conveniently add an automatic driving function to the manually driven vehicle, and the automatic driving function can be realized only by receiving the automatic driving scheme and executing according to the automatic driving scheme because the scheme obtained by the automatic driving information acquisition and analysis is not required to be completed by the vehicle.
A third embodiment of the present invention is seen in fig. 3. Fig. 3 is a schematic diagram of an intelligent transportation system according to a third embodiment of the present invention. As shown, the intelligent transportation system includes: a road monitoring device 31, a signal transceiver 32 and a server 33.
The road monitoring device 31 is used for acquiring road information and road condition information, and may include; may include cameras, radar, inductive sensors, etc. The intelligent transportation system may further include: high altitude monitoring devices such as satellite, aircraft, unmanned aerial vehicle, high altitude balloon.
The signal transceiver 32 is used for transceiving signals. The information acquired by the road monitoring device 31 may be transmitted to the server 33, the information sent by the vehicle, such as vehicle-mounted monitoring information, may be transmitted to the server 33, and the server 33 may transmit the information to the vehicle on the road, such as a road vehicle overall automatic driving scheme.
The server 33 acquires road information, road condition information and vehicle information running on the road; establishing a road traffic model through the acquired information, and intelligently analyzing an overall automatic driving scheme of the road vehicle; the protocol is transmitted to the autonomous vehicle via the signal transceiver 32, and the autonomous vehicle executes autonomous driving in accordance with the protocol.
The server 33 may also obtain information from other sources such as a high-altitude monitoring device, or may be connected to the internet to obtain information from databases of traffic control departments or other related organizations. The server 33 may be a server of the intelligent transportation system itself, a cloud server, or an MEC server, and realizes a large computation amount real-time computation by using edge computation and a 5G high-speed network.
The intelligent traffic system is formed by adopting the automatic driving method based on the intelligent traffic system, the structural characteristics are in one-to-one correspondence, and the description of the automatic driving method based on the intelligent traffic system can be referred to, and is not repeated herein.
In summary, according to the automatic driving method and device based on the intelligent transportation system and the intelligent transportation system, the automatic driving method comprises the steps of obtaining road information, road condition information and vehicle information, establishing a road transportation model, and intelligently analyzing the execution of the whole automatic driving scheme of the road vehicle. The autopilot device includes a control assembly and a wireless transmission assembly. The intelligent transportation system includes: road monitoring devices, signal transceiver and server. According to the automatic driving method and device based on the intelligent traffic system and the intelligent traffic system, the road information, the road condition information and the vehicle information are comprehensively acquired through the road monitoring device, the vehicle-mounted monitoring device or other monitoring devices, and an integral automatic driving scheme is provided for vehicles on the road, so that the problems of incomplete information acquisition, mutual conflict of vehicle decisions and the like are effectively avoided, the driving safety and the driving efficiency of the vehicles are greatly improved, and the function of adding manually driven vehicles into automatic driving can be conveniently realized at low cost.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (12)
1. An automatic driving method based on an intelligent transportation system is characterized by comprising the following steps:
the intelligent traffic system acquires road information and road condition information;
the system acquires information of vehicles running on a road;
the system establishes a road traffic model and intelligently analyzes the whole automatic driving scheme of the road vehicle;
the system transmits the plan to an autonomous vehicle, which executes autonomous driving according to the plan.
2. The automatic driving method according to claim 1, wherein the means for acquiring the traffic information by the intelligent transportation system comprises: at least one approach of a road monitoring device, a vehicle-mounted monitoring device or a high-altitude monitoring device.
3. The automated driving method of claim 1, wherein: the road information includes: the number of lanes, lane width, curvature radius, gradient, road material, entrance and exit, traffic lights, road junctions, connecting roads, road environment, and road surface condition.
4. The automated driving method of claim 1, wherein: the method further includes obtaining information about the human-driven vehicle on the road for predicting a driving expectation behavior of the human-driven vehicle.
5. The automated driving method of claim 1, wherein: the method further comprises transmitting the road vehicle overall automatic driving scheme to a manually driven vehicle for prompting a driver of a driving situation of the automatically driven vehicle or guiding the driver to drive the vehicle.
6. The automated driving method of claim 1, wherein: the automatic driving method further includes: under the same road traffic model, the overall automatic driving schemes of different road vehicles are evaluated and compared, and the optimal overall automatic driving scheme of the road vehicles is preferentially provided.
7. The automated driving method according to any one of claims 1 to 6, wherein: and when the road traffic model changes, carrying out intelligent analysis again to obtain a new road vehicle integral automatic driving scheme, and transmitting the scheme to an automatic driving vehicle for execution.
8. The automated driving method according to any one of claims 1 to 6, wherein: the method also comprises the step of selecting a proper coverage area to establish a road traffic model according to the actual conditions of the road information and the road condition information.
9. The automated driving method according to any one of claims 1 to 6, wherein: the method further comprises converting and/or integrating the acquired information of different data sources/data structures/data standards/data formats/data descriptions.
10. The automated driving method according to any one of claims 1 to 9, wherein: the road traffic model is established by selecting the existing road traffic model with high similarity to the actual situation and correspondingly modifying the existing road traffic model according to the actual information to obtain the road traffic model suitable for the actual situation.
11. An autopilot device for a vehicle, the autopilot device comprising a control assembly and a wireless transmission assembly;
the wireless transmission assembly is used for wirelessly transmitting monitoring data of the vehicle-mounted monitoring device and receiving an automatic driving scheme;
the control component is used for controlling the vehicle to run according to the automatic driving scheme.
12. An intelligent transportation system, characterized in that the system comprises: a road monitoring device, a signal transceiver and a server,
the road monitoring device is used for acquiring road information and road condition information;
the signal transceiver is used for transceiving signals;
the server acquires road information, road condition information and vehicle information running on the road; establishing a road traffic model through the acquired information, and intelligently analyzing an overall automatic driving scheme of the road vehicle; and transmitting the scheme to an automatic driving vehicle through the signal transceiver, and executing automatic driving by the automatic driving vehicle according to the scheme.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2020/094719 WO2021243710A1 (en) | 2020-06-05 | 2020-06-05 | Intelligent transportation system-based automatic driving method and device, and intelligent transportation system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113330497A true CN113330497A (en) | 2021-08-31 |
Family
ID=77413320
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202080000912.2A Pending CN113330497A (en) | 2020-06-05 | 2020-06-05 | Automatic driving method and device based on intelligent traffic system and intelligent traffic system |
| CN202180001446.4A Pending CN113993761A (en) | 2020-06-05 | 2021-06-05 | Automatic vehicle driving method, device and system based on intelligent transportation system |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202180001446.4A Pending CN113993761A (en) | 2020-06-05 | 2021-06-05 | Automatic vehicle driving method, device and system based on intelligent transportation system |
Country Status (2)
| Country | Link |
|---|---|
| CN (2) | CN113330497A (en) |
| WO (2) | WO2021243710A1 (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114664084A (en) * | 2022-03-02 | 2022-06-24 | 河南职业技术学院 | Intelligent transportation system with face recognition function |
| CN115662166A (en) * | 2022-09-19 | 2023-01-31 | 长安大学 | Automatic driving data processing method and automatic driving traffic system |
| CN117475625A (en) * | 2023-10-30 | 2024-01-30 | 深圳市领航者汽车智能技术开发有限公司 | Driving assistance system and method based on artificial intelligence |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115086209B (en) * | 2022-05-10 | 2024-03-12 | 浙江众合科技股份有限公司 | Signal system digital intelligent test method based on edge computing platform |
| CN115311880B (en) * | 2022-08-03 | 2024-03-19 | 九识(苏州)智能科技有限公司 | Distributed visual field enhancement method for low-speed automatic driving vehicle |
| CN115331436B (en) * | 2022-08-04 | 2024-04-30 | 西安理工大学 | Road section automatic driving automobile motion planning method considering pedestrian safety |
| CN115649198A (en) * | 2022-11-03 | 2023-01-31 | 中科天极(新疆)空天信息有限公司 | Automatic vehicle driving method and system |
| CN116878529A (en) * | 2023-07-10 | 2023-10-13 | 上海朗尚传感技术有限公司 | Dual-antenna navigation route deviation early warning system based on Beidou technology |
| CN117558129B (en) * | 2023-11-14 | 2024-06-11 | 长沙云软信息技术有限公司 | Intelligent positioning system and method for expressway construction |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101799977A (en) * | 2009-12-31 | 2010-08-11 | 马正方 | Intelligent traffic system |
| US20150266455A1 (en) * | 2013-12-06 | 2015-09-24 | Christopher Kenneth Wilson | Systems and Methods for Building Road Models, Driver Models, and Vehicle Models and Making Predictions Therefrom |
| CN107272683A (en) * | 2017-06-19 | 2017-10-20 | 中国科学院自动化研究所 | Parallel intelligent vehicle control based on ACP methods |
| CN108961749A (en) * | 2018-07-12 | 2018-12-07 | 南方科技大学 | Intelligent traffic system and intelligent traffic control method |
| CN109686118A (en) * | 2019-01-24 | 2019-04-26 | 嘉兴行适安车联网信息科技有限公司 | A kind of economic control loop and its drive manner based on the collaboration of people's bus or train route |
| CN110503823A (en) * | 2019-07-11 | 2019-11-26 | 江苏大学 | A kind of automatic driving vehicle dedicated Lanes control system and method |
| US20200090511A1 (en) * | 2018-09-19 | 2020-03-19 | Baidu Online Network Technology (Beijing) Co., Ltd. | Method and device for assisting in controlling automatic driving of vehicle, and system |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103065487A (en) * | 2012-12-19 | 2013-04-24 | 深圳市元征科技股份有限公司 | Internet of vehicles intelligent transportation system and vehicle mounted terminal |
| CN104269053A (en) * | 2014-08-29 | 2015-01-07 | 陈业军 | Intelligent traffic system and method and intelligent automobile |
| CN106355880B (en) * | 2016-10-09 | 2018-08-21 | 东南大学 | A kind of automatic driving vehicle control parameter scaling method towards with vehicle safety |
| CN106710203A (en) * | 2017-01-10 | 2017-05-24 | 东南大学 | Multidimensional intelligent network connection traffic system |
| CN107045782A (en) * | 2017-03-05 | 2017-08-15 | 赵莉莉 | Intelligent transportation managing and control system differentiation allocates the implementation method of route |
| CN106940933B (en) * | 2017-03-08 | 2019-05-24 | 北京理工大学 | A kind of intelligent vehicle decision lane-change method based on intelligent transportation system |
| CN108109413B (en) * | 2017-12-13 | 2020-06-26 | 厦门金龙旅行车有限公司 | Information interaction system for unmanned vehicle and non-intelligent traffic participants |
| JP6841263B2 (en) * | 2018-05-24 | 2021-03-10 | 株式会社デンソー | Travel plan generator, travel plan generation method, and control program |
| CN108877268B (en) * | 2018-08-07 | 2021-05-25 | 南京大学 | Unmanned-oriented traffic-light-free crossroad intelligent scheduling method |
| CN110969837B (en) * | 2018-09-30 | 2022-03-25 | 毫末智行科技有限公司 | Road information fusion system and method for automatic driving vehicle |
| CN110045729B (en) * | 2019-03-12 | 2022-09-13 | 北京小马慧行科技有限公司 | Automatic vehicle driving method and device |
-
2020
- 2020-06-05 CN CN202080000912.2A patent/CN113330497A/en active Pending
- 2020-06-05 WO PCT/CN2020/094719 patent/WO2021243710A1/en active Application Filing
-
2021
- 2021-06-05 CN CN202180001446.4A patent/CN113993761A/en active Pending
- 2021-06-05 WO PCT/CN2021/098508 patent/WO2021244655A1/en active Application Filing
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101799977A (en) * | 2009-12-31 | 2010-08-11 | 马正方 | Intelligent traffic system |
| US20150266455A1 (en) * | 2013-12-06 | 2015-09-24 | Christopher Kenneth Wilson | Systems and Methods for Building Road Models, Driver Models, and Vehicle Models and Making Predictions Therefrom |
| CN107272683A (en) * | 2017-06-19 | 2017-10-20 | 中国科学院自动化研究所 | Parallel intelligent vehicle control based on ACP methods |
| CN108961749A (en) * | 2018-07-12 | 2018-12-07 | 南方科技大学 | Intelligent traffic system and intelligent traffic control method |
| US20200090511A1 (en) * | 2018-09-19 | 2020-03-19 | Baidu Online Network Technology (Beijing) Co., Ltd. | Method and device for assisting in controlling automatic driving of vehicle, and system |
| CN109686118A (en) * | 2019-01-24 | 2019-04-26 | 嘉兴行适安车联网信息科技有限公司 | A kind of economic control loop and its drive manner based on the collaboration of people's bus or train route |
| CN110503823A (en) * | 2019-07-11 | 2019-11-26 | 江苏大学 | A kind of automatic driving vehicle dedicated Lanes control system and method |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114664084A (en) * | 2022-03-02 | 2022-06-24 | 河南职业技术学院 | Intelligent transportation system with face recognition function |
| CN115662166A (en) * | 2022-09-19 | 2023-01-31 | 长安大学 | Automatic driving data processing method and automatic driving traffic system |
| CN115662166B (en) * | 2022-09-19 | 2024-04-09 | 长安大学 | Automatic driving data processing method and automatic driving traffic system |
| CN117475625A (en) * | 2023-10-30 | 2024-01-30 | 深圳市领航者汽车智能技术开发有限公司 | Driving assistance system and method based on artificial intelligence |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113993761A (en) | 2022-01-28 |
| WO2021243710A1 (en) | 2021-12-09 |
| WO2021244655A1 (en) | 2021-12-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113330497A (en) | Automatic driving method and device based on intelligent traffic system and intelligent traffic system | |
| CN110550029B (en) | Obstacle avoiding method and device | |
| US10606270B2 (en) | Controlling an autonomous vehicle using cost maps | |
| CN112859830B (en) | Design operation region ODD judgment method, device and related equipment | |
| CN110989569B (en) | Vehicle running control method and related equipment | |
| EP4184476A1 (en) | Method and device for controlling switching of vehicle driving mode | |
| CN113128303A (en) | Automatic driving method, related equipment and computer readable storage medium | |
| US20220105959A1 (en) | Methods and systems for predicting actions of an object by an autonomous vehicle to determine feasible paths through a conflicted area | |
| CN113160547B (en) | Automatic driving method and related equipment | |
| CN112672942B (en) | Vehicle lane changing method and related equipment | |
| CN112512887B (en) | Driving decision selection method and device | |
| CN111971721A (en) | Vehicle combined driving method and system based on intelligent traffic system and moped | |
| CN114792149A (en) | Track prediction method and device and map | |
| CN113954858A (en) | Method for planning vehicle driving route and intelligent automobile | |
| CN114655260B (en) | Control system of unmanned tourist car | |
| CN113022573B (en) | Road structure detection method and device | |
| CN114813157A (en) | Test scene construction method and device | |
| CN112654547A (en) | Driving reminding method, device and system | |
| WO2022151839A1 (en) | Vehicle turning route planning method and apparatus | |
| CN114556251B (en) | Method and device for determining a passable space for a vehicle | |
| WO2023102827A1 (en) | Path constraint method and device | |
| US20240239359A1 (en) | System, Method, and Computer Program Product for Data-Driven Optimization of Onboard Data Collection | |
| US20230015880A1 (en) | Using distributions for characteristics of hypothetical occluded objects for autonomous vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210831 |