Provided is an electronic control device that estimates a presence of a vehicle incapable of performing vehicle-to-vehicle communication ahead of a communication partner vehicle is estimated.

An electronic control device 30 detects, on map data, an intersection 60 located ahead in a traveling direction of a host vehicle 10 based on location data and the map data of the host vehicle 10, calculates a stop position (first position) 71 where a communication partner vehicle 11 is supposed to pass through or stop when the communication partner vehicle 11 stops at the intersection 60, based on vehicle information of the communication partner vehicle 11 that is traveling toward the intersection 60, and determines whether or not there is another vehicle 13 ahead in the traveling direction of the communication partner vehicle 11, based on the stop position (first position) 71 and an intersection area 20 (second position 72) set before the intersection 60.

An automated driving trajectory generating device is configured to: recognize a mobile object which is located near a vehicle; calculate a host vehicle path for automated driving of the vehicle and a plurality of predicted paths of the mobile object based on a position of the vehicle on a map, a position of the mobile object on the map, and map information; calculate a predicted acceleration which is generated in the mobile object moving along a predicted path for each predicted path based on the plurality of predicted paths and a vehicle speed of the mobile object; identify a target path which is a predicted path used to generate the trajectory out of the plurality of predicted paths based on a result of comparison between the predicted acceleration and an acceleration threshold value; and generate the trajectory based on the host vehicle path and the target path.

Systems and methods for managing environmental conditions for an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes a perception sensor configured to generate perception data indicative of a condition of the environment, a network communication transceiver configured to communicate with an oversight system and an external weather condition source, a non-transitory computer readable medium, and a processor. The processor is configured to: receive the perception data from the at least one perception sensor, receive an indication of current weather conditions from the external weather condition source, determine a current environmental condition severity level from a plurality of severity levels based on the perception data and the indication of current weather conditions, modify one or more driving parameters that that govern a range of actions that can be autonomously executed by the autonomous vehicle, and navigate the autonomous vehicle based on the modified driving parameters.

An autonomous vehicle (AV) includes a vehicle computing system including one or more processors programmed to receive map data associated with a map of a geographic location, determine, based on the map data, one or more local routes in the one or more roadways between the current location of the AV and one or more exit locations, and control travel of the AV based on a selected local route of the one or more local routes. The map includes one or more roadways in the geographic location. The map data includes a global route in the one or more roadways between a current location of the AV and a destination location of the AV. The one or more exit locations are located between the current location of the AV and the destination location of the AV.

An autonomous vehicle navigates an intersection in which occlusions block the vehicle's ability to detect moving objects. The vehicle handles this by generating a phantom obstacle behind the occlusion. The vehicle will predict the speed of the phantom obstacle and use the predicted speed to assess whether the phantom obstacle may collide with the vehicle. If a collision is a risk, the vehicle will slow or stop until it confirms that either (a) the phantom obstacle is not a real obstacle or (b) the vehicle can proceed at a speed that avoids the collision. To determine which occlusions shield real objects, the system may use a rasterized visibility grid of the area to identify occlusions that may accommodate the object.

Aspects of the disclosure relate to controlling a transition between a manual driving mode and an autonomous driving mode of a vehicle. For instance, one or more processors of one or more control computing devices may control the vehicle in the autonomous driving mode. While controlling the vehicle in the autonomous driving mode and decelerating at a given rate, the processors may receive at a user input of the vehicle input requesting a transition from the autonomous driving mode to the manual driving mode. In response to the input, the processors may transition the vehicle to the manual driving mode. After transitioning the vehicle to the manual driving mode, the processors may send deceleration signals to a deceleration actuator thereby causing the vehicle to continue to decelerate at the given rate.

Provided is an in-vehicle system in which an own vehicle does not decelerate according to a preceding vehicle even if the preceding vehicle changes lanes to the right turn lane or the left turn lane and decelerates after own vehicle entering an intersection area. A CPU 107 (first control unit) controls the own vehicle to follow the preceding vehicle. An image processing circuit 106 (detection unit) detects a lane marking of the traveling lane. The CPU 107 (determination unit) determines whether an own vehicle 201 has entered the intersection area. When the detection of one of the right and left lane markings of the traveling lane is interrupted (S20: YES) after the own vehicle 201 has entered the intersection area (S15: YES), the CPU 107 (second control unit) releases ACC that causes the own vehicle 201 to follow the preceding vehicle 202.

In one embodiment, a computer-implemented method of operating an autonomous driving vehicle (ADV) includes perceiving a driving environment surrounding the ADV based on sensor data obtained from one or more sensors mounted on the ADV, determining a driving scenario, in response to a driving decision based on the driving environment, applying a predetermined machine-learning model to data representing the driving environment and the driving scenario to generate a set of one or more driving parameters, and planning a trajectory to navigate the ADV using the set of the driving parameters according to the driving scenario through the driving environment.

A driving assistance method includes: detecting a first other vehicle entering an intersection on a first route where a host vehicle is traveling from a second route; predicting whether or not the first other vehicle will stop in the intersection, and predicting a stop position of the first other vehicle; calculating a minimum distance of a first gap between a vehicle body of the first other vehicle and a surrounding object around the first other vehicle or between the vehicle body of the first other vehicle and a road edge of a travel lane of the first other vehicle when the first other vehicle stops at the predicted stop position; and predicting according to the calculated minimum distance whether or not a second other vehicle, which is a following vehicle behind the first other vehicle, may slip through the first gap from behind the first other vehicle.

Systems and methods for controlling navigation of an autonomous vehicle for making an unprotected turn while traversing an intersection. The methods may include identifying a loiter pose of an autonomous vehicle for stopping at a point in an intersection before initiating an unprotected turn, initiating navigation of the autonomous vehicle to the loiter pose when a traffic signal is at a first state, determining whether the traffic signal has changed to a second state during or after navigation of the autonomous vehicle to the loiter pose, and in response to determining that the traffic signal has changed to the second state, generating a first trajectory for navigating the autonomous vehicle to execute the unprotected turn if the expected time for moving the autonomous vehicle from a current position to a position when the autonomous vehicle has fully exited an opposing conflict lane is less than a threshold time.