A vehicle control device includes a recognizer configured to recognize a surrounding environment including a structure of a road near a vehicle and another vehicle, a deriver configured to derive a predicted probability that the other vehicle will travel in the future along each of routes which are assumed when a plurality of routes along which the other vehicle is able to travel are assumed on a road on which the other vehicle recognized by the recognizer travels, and a travel controller configured to control behavior of the vehicle based on the predicted probability derived by the deriver.

A vehicle control device includes a processor. The processor is configured to: detect a mobile body located in a region that is not a host vehicle lane in which a host vehicle is traveling, out of regions that are adjacent to an adjacent lane that is adjacent to the host vehicle lane; detect entry operation of the mobile body to enter the adjacent lane; and when the processor detects the entry operation, perform steering control for the host vehicle such that a travel position of the host vehicle within the host vehicle lane in a width direction of the host vehicle lane is moved in a direction away from the adjacent lane before the entry operation is completed.

A cross-traffic warning system for a motor vehicle includes first and second input devices transmitting associated first and second input signals for first and second detected objects positioned on the roadway. The system further includes a computer having one or more processors and a computer readable medium storing instructions. The processor is programmed to determine that the first object is a Vulnerable Road User (“VRU”) travelling on a first path based on the first input signal. The processor is further programmed to determine that the second object is a third party vehicle and further that the VRU and the third party vehicle are travelling on an associated one of first and second paths to imminently collide with one another based on the first and second input signals. The processor is further programmed to generate an actuation signal in response to the processor determining the imminent collision.

A method and an electronic device for generating a trajectory of a Self-Driving Car (SDC) are provided. The method comprises: determining a presence of at least one third-party object around the SDC; generating a plurality of predicted trajectories for the third-party object, where at least one of the plurality of trajectories includes a maneuver executable, by the third-party object, at a future third-party object location; calculating, for the at least one of the plurality of trajectories including the a respective braking profile associated with the third-party object; in response to the respective braking profile being above a pre-determined threshold, eliminating an associated one of the at least one of the plurality of trajectories from future processing; determining an SDC trajectory based on remaining ones of the plurality of predicted trajectories for the third-party.

A refuse vehicle comprising a chassis, a body assembly coupled to the chassis, the body assembly defining a refuse compartment, one or more sensors coupled to the body and configured to provide data relating to the presence of an obstacle within an area near the refuse vehicle, a controller configured to receive the data from the one or more sensors, determine, using an obstacle detector and the data, the presence of an obstacle within the area and initiate a control action, wherein the control action includes at least one of controlling the movement of the refuse vehicle, controlling the movement of a lift assembly attached to the body assembly, or generating an alert.

The techniques and systems herein enable collision indication based on yaw rate and lateral velocity thresholds. Specifically, an in-path band is determined for a predicted path of a host vehicle. Responsive to determining that a target is within the in-path band, a lateral movement for the host vehicle at a time is determined based on whether a yaw rate of the host vehicle meets a yaw rate threshold. A lateral movement for the target at the time is also determined based on whether a lateral velocity of the target meets a lateral velocity threshold. A collision indication is generated responsive to determining, based on the lateral movements, that the host vehicle and the target are likely to be within the in-path band at the time. In this way, the collision indication more accurately reflects an imminent collision, thereby increasing safety while also mitigating false-positive events.

This disclosure is directed to techniques for identifying relevant objects within an environment. For instance, a vehicle may use sensor data to determine a candidate trajectory associated with the vehicle and a predicted trajectory associated with an object. The vehicle may then use the candidate trajectory and the predicted trajectory to determine an interaction between the vehicle and the object. Based on the interaction, the vehicle may determine a time difference between when the vehicle is predicted to arrive at a location and when the object is predicted to arrive at the location. The vehicle may then determine a relevance score associated with the object using the time difference. Additionally, the vehicle may determine whether to input object data associated with the object into a planner component based on the relevance score. The planner component determines one or more actions for the vehicle to perform.

Systems and methods for navigating a host vehicle are disclosed. In one implementation at least one processor is programmed to receive two or more images captured by a camera of the host vehicle from an environment of the host vehicle; analyze the two or more images to identify a representation of at least a portion of a first object and a representation of at least a portion of a second object; determine a first region of the two or more images associated with the first object and a type of the first object; and determine a second region of the two or more images associated with the second object and type of the second object, wherein the type of the first object is different from the type of the second object.

A method is intended to regulate the speed of an at least partially automated vehicle traveling on a first traffic lane adjacent to a second traffic lane. This method comprises a step (10-80) in which, if a radius of curvature of a future portion representative of a bend is detected, a phase of deceleration to a first deceleration speed adapted to the radius of curvature is imposed on the first vehicle unless it is in the course of overtaking a second vehicle traveling in the second traffic lane, since in that case a current speed of the second vehicle is determined and then, if the first deceleration speed is less than this current speed, the first deceleration speed is replaced with a second deceleration speed greater than this current speed.

A biased driving system and a biased driving method are capable of controlling biased driving of a vehicle in consideration of host vehicle location recognition accuracy, nearby vehicle risk, driving style, road curvature, and road shape. The biased driving system includes a control parameter creation unit configured to extract an object causing biasing of a host vehicle using converged object information, which is map information including location and speed of a vehicle around the host vehicle, to create an imaginary line of the extracted object by reflecting at least one of risk of a nearby vehicle, location recognition accuracy, driving style of a driver of the host vehicle, road curvature, or road shape, and to create a control parameter using the imaginary line.