According to one embodiment, a motion trajectory boundary is obtained based on a trajectory that has been planned to drive an ADV for a next time period. A safe driving area boundary is determined for the ADV based on perception data perceiving a driving environment surrounding the ADV. The motion trajectory boundary and the safe drivable area boundary are projected onto a map such as an HD map. A relative location of the ADV within the map relative to the motion trajectory and the safe drivable area boundary is determined. A fail-safe action or a fail operational action may be performed based on the relative location of the ADV in view of the motion trajectory boundary and the safe drivable area boundary.

The present disclosure relates to a pull up controlling system of a vehicle for controlling the vehicle in a planned stop-and-go situation. The pull up controlling system determines a position of the vehicle and identifies a stop-and-go destination within a predeterminable distance from the vehicle position. The stop-and-go destination includes an interaction interface. The pull up controlling system provides a user input request relating to whether to discard or acknowledge the stop-and-go destination. The pull up controlling system receives intention data indicative of confirmation to acknowledge the stop-and-go destination. The pull up controlling system further derives preference data indicative of an openable section of the vehicle. The pull up controlling system maneuvers the vehicle to pull up at the stop-and-go destination, with the openable section aligned with the interaction interface.

A transportation vehicle, a traffic control entity, a method, a computer program, and an apparatus for adapting a speed of transportation vehicles in a platoon. The method for adapting a speed of transportation vehicles in a platoon includes obtaining information related to a future course of required minimum inter-vehicular distances of the transportation vehicles of the platoon. The method also includes adapting a speed of the transportation vehicles of the platoon based on the information related to the future course of the required minimum inter-vehicular distances and a fuel consumption of the transportation vehicles of the platoon.

A system comprises a computer having a processor and a memory, the memory storing instructions executable by the processor to access sensor data of a sensor of a vehicle while an adaptive cruise control feature of the vehicle is active, detect, based on the sensor data, an object located along a path of travel of the vehicle, determine that the object is a moveable object based on a radar return of a radar reflector of the object, and responsive to the determination that the object is the moveable object, adjust, by the adaptive cruise control feature, the speed of the vehicle.

A vehicle system includes an electronic control unit. The electronic control unit is configured to execute a first program, a second program, and a third program. The first program is configured to recognize an object present around a vehicle, the second program is configured to store information related to the recognized object as time-series map data, and the third program is configured to predict a future position of the object based on the stored time-series map data. The first program and the third program are configured to be (i) first, individually optimized based on first training data corresponding to output of the first program and second training data corresponding to output of the third program, and (ii) then, collectively optimized based on the second training data corresponding to the output of the third program.

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.

A driving system for a motor vehicle includes a first driving function for automated driving with automated longitudinal and transverse guidance and a second driving function for automated driving with at least automated longitudinal guidance, or with at least automated transverse guidance. The second driving function has a lower degree of automation than the first driving function. The first driving function is available in a tolerance range. Starting from a driving state with an active second driving function and a value of the driving parameter outside the tolerance range, the driving system changes, when the second driving function is active, the value of the driving parameter in the direction of the tolerance range via automated longitudinal guidance or automated transverse guidance. The driving system then determines that the driving parameter satisfies a criterion with respect to the tolerance range.

A driving support apparatus according to the invention estimates the position of a moving body by controlling a position estimation unit when the tracking-target moving body leaves a first area or a second area to enter a blind spot area and detects the position of the moving body by controlling a position detection unit when the moving body leaves the blind spot area to enter the first area or the second area. In this manner, the trajectory of the tracking-target moving body is calculated so that the trajectory of the moving body detected in the first area or the second area and the trajectory of the moving body estimated in the blind spot area are continuous to each other and driving support is executed based on the calculated trajectory of the tracking-target moving body.

Example embodiments relate to techniques for three dimensional (3D) object detection and localization. A computing system may cause a radar unit to transmit radar signals and receive radar reflections relative to an environment of a vehicle. Based on the radar reflections, the computing system may determine a heading and a range for a nearby object. The computing system may also receive an image depicting a portion of the environment that includes the object from a vehicle camera and remove peripheral areas of the image to generate an image patch that focuses upon the object based on the heading and the range for the object. The image patch and the heading and the range for the object can be provided as inputs into a neural network that provides output parameters corresponding to the object, which can be used to control the vehicle.

A current lane of vehicle operation is determined to be branched at a location into a through lane and a deceleration lane based on first sensor data indicating an increased width of the current lane exceeds a predetermined width at the location. Then the vehicle is determined to be operating in one of (a) the deceleration lane, or (b) the through lane, based on second sensor data. Then one of (a) an assist feature of the vehicle is activated to a disabled state based on determining the vehicle is in the deceleration lane, or (b) the assist feature is maintained in an enabled state based on determining the vehicle is in the through lane.