A method for traffic light auto-labeling includes aggregating vehicle-to-infrastructure (V2I) traffic light signals at an intersection to determine transition states of each driving lane at the intersection during operation of an ego vehicle. The method also includes automatically labeling image training data to form auto-labeled image training data for a traffic light recognition model within the ego vehicle according to the determined transition states of each driving lane at the intersection. The method further includes planning a trajectory of the ego vehicle to comply with a right-of-way according to the determined transition states of each driving lane at the intersection according to a trained traffic light detection model. A federated learning module may train the traffic light recognition model using the auto-labeled image training data during the operation of the ego vehicle.

A vehicle control method of an autonomous vehicle for a right and left turn at a crossroad includes: determining whether a second vehicle intends to change a lane while passing a front or a rear of a first vehicle in order to move to a target lane for the right and left turn at the crossroad; controlling the first vehicle to decelerate when it is determined that the second vehicle intends to change the lane while passing the front of the first vehicle; determining whether the second vehicle is entering the first lane toward the front or the rear of the first vehicle; calculating a steering amount of the second vehicle when it is determined that the second vehicle is entering the first lane toward the front of the first vehicle; and controlling the first vehicle to decelerate according to the steering amount.

An autonomous driving system acquires information concerning a vehicle density in an adjacent lane that is adjacent to a lane on which an own vehicle is traveling, when the own vehicle travels on a road having a plurality of lanes. The autonomous driving system selects the adjacent lane as an own vehicle travel lane, when the vehicle density in the adjacent lane that is calculated from the acquired information is lower than a threshold density that is determined in accordance with relations between the own vehicle and surrounding vehicles. The autonomous driving system performs lane change to the adjacent lane autonomously, or propose lane change to the adjacent lane to a driver, when the adjacent lane is selected as the own vehicle travel lane.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. Example embodiments disclosed herein provide enhanced high-precision operation of an AV in low-speed environments, such as a toll booth facility or heavy traffic. One example method disclosed herein includes a control computer identifying a starting point of the toll booth facility on the roadway and a plurality of toll lanes associated with the toll booth facility; selecting a particular toll lane; determining a trajectory for the AV that extends through the particular toll lane; and in response to the autonomous vehicle arriving at the starting point for the toll booth facility, transmitting, over a subsystem interface to one or more drive subsystems of the AV, instructions configured to cause the drive subsystems to operate together to cause the AV to travel according to the trajectory.

A matching system that matches a first vehicle requiring substitution when being at least either loaded into or unloaded from a parking place and a remote driver driving the first vehicle as a substitute through remote operation includes a terminal and a server. The terminal transmits substitution request information to the server. The server that has received the request information transmits waiting time information to the terminal. The terminal notifies a user or a staff of the received waiting time information, accepts a waiting time information approval or additional fee payment instructions, and settles the additional fee through cooperation with the server, upon receiving the payment instructions. The server changes the turn of the first vehicle in a queue for the remote operation service such that the first vehicle is prioritized more than a second vehicle that has not paid the additional fee, upon completing the additional fee settlement.

A vehicle trajectory prediction device is provided. The vehicle trajectory prediction device includes a transceiver, at least one processor, and at least one memory operatively connected with the at least one processor to store at least one instruction causing the at least one processor to perform operations. The operations receive first trajectory data for an ego-vehicle and second trajectory data for at least one neighbor-vehicle, obtain a first feature vector from a first extractor and obtain a second feature vector from a second extractor, obtain an interdependency feature vector between the ego-vehicle and the at least one neighbor-vehicle from a third extractor having mapping data generated by mapping the second feature vector to the second trajectory data as input data, and generate predicted trajectory data of the ego-vehicle from a trajectory generator having the first feature vector and the interdependency feature vector as input data.

Vehicle alert and control systems and methods taking into account a detected road friction at a following vehicle and a predicted road friction by the following vehicle. The detected road friction between the following vehicle tires and the road surface may be assessed using a variety of methodologies and is used to compute a critical safety distance between the following vehicle and the preceding vehicle and a critical safety speed of the following vehicle. The predicted road friction ahead of the following vehicle may also be assessed using a variety of methodologies (lidar, camera, and cloud-based examples are provided) and is used to compute a warning safety distance between the following vehicle and the preceding vehicle and a warning safety speed of the following vehicle. These functionalities may be applied to vehicle/stationary object warning and response scenarios as well.

In an autonomous driving method, a health physiological data range is added to an operational design domain (ODD) deployed on an autonomous driving system (ADS) as an applicable range of the ODD. The ADS receives real-time physiological data of a driver/passenger collected by a monitoring device. When a difference between the real-time physiological data and a health physiological data range is greater than a preset value, and a duration in which the real-time physiological data deviates from the health physiological data range is greater than a first preset duration, the ADS degrades an autonomous driving service being executed by an autonomous driving vehicle, and executing a first driving policy based on the difference and the duration.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a driving scenario machine learning network and providing a simulated driving environment. One of the operations is performed by receiving video data that includes multiple video frames depicting an aerial view of vehicles moving about an area. The video data is processed and driving scenario data is generated which includes information about the dynamic objects identified in the video. A machine learning network is trained using the generated driving scenario data. A 3-dimensional simulated environment is provided which is configured to allow an autonomous vehicle to interact with one or more of the dynamic objects.

Systems and techniques are described for consuming data in an intelligent transport system. In some implementations, a system includes a display screen device and sensors. The sensors generates data describing sensor observations of a roadway at a first location and provides data describing the observations to the display screen device. The display screen device receives the data and determines an event and a type of the event. The display screen device displays second data indicative of the type of event, the second data being of a format that is consumable by a sensor on a vehicle traversing the roadway towards the first location, the sensor (i) located within a first resolution distance from the display screen device and (ii) located outside a second resolution distance of detecting the event, wherein the second data is used by an on-board processing system of the vehicle to adjust its driving behavior.