Systems and methods for selecting a trajectory for an autonomous vehicle based on perceived risk are provided. A trajectory is selected that satisfies objective safety constraints and minimizes an overall cost based at least in part on cost components representing the perceived risk of the trajectory. The vehicle is navigated according to the selected (e.g., optimized) trajectory.

A method for inertia drive control is provided. The method includes performing advanced inertia drive control by an inertia drive controller. The controller detects a speed reduction event during road driving of a vehicle, lane division together with road type division for a road, and performs inertia drive control guide and the inertia drive control based on drive conditions of lane change and lane maintenance.

Aspects of the disclosure relate to identifying and adjusting speed plans for controlling a vehicle in an autonomous driving mode. In one example, an initial speed plan for controlling speed of the vehicle for a first predetermined period of time corresponding to an amount of time along a route of the vehicle is identified. Data identifying an object and characteristics of that object is received from a perception system of the vehicle. A trajectory for the object that will intersect with the route at an intersection point at particular point in time is predicted using the data. A set of constraints is generated based on at least the trajectory. The speed plan is adjusted in order to satisfy the set of constraints for a second predetermined period of time corresponding to the amount of time. The vehicle is maneuvered in the autonomous driving mode according to the adjusted speed plan.

The present application relates to a method and apparatus for real time lateral control and steering actuation assessment for a motor vehicle by determining a desired host vehicle path, establishing a control point along the host vehicle path, generating a motion path between a host vehicle location and the control point, estimating a theoretical acceleration of the host vehicle along the motion path, controlling the host vehicle along the motion path, receiving a measured acceleration from an inertial measurement unit within the host vehicle, generating an error term in response to a comparison of the motion path and the measured lateral travel, and adjusting the performance limits of the sub-features in response to the error term.

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.

Systems and methods for controlling navigation of an autonomous vehicle through an intersection are disclosed. The methods include determining a loiter pose of an autonomous vehicle for stopping at a point within the intersection before initiating an unprotected turn for traversing the intersection. One or more distinct classes of trajectories are then identified, each of which is associated with multiple trajectories that take the same combination of discrete actions with respect to the loiter pose. A constraint set for each of the one or more distinct classes of trajectories is then be computed based on the loiter pose, and a candidate trajectory is determined for each of the one or more distinct classes based on the corresponding constraint set. A trajectory for the autonomous vehicle for executing the unprotected turn for traversing the intersection is selected from amongst the candidate trajectories.

A control device (100) can communicate with a first sensor (300) for detecting an object around a first vehicle and is equipped on the first vehicle (500). The control device (100) includes a first acquisition unit, a second acquisition unit, a detection unit, and a determination unit. The first acquisition unit acquires a sensing result being a result of detecting an object around the first vehicle (500) from the first sensor (300) equipped on the first vehicle (500). The second acquisition unit acquires positional information of a specified object being an object for performance measurement of the first sensor (300). The detection unit detects the specified object existing within a reference distance from the first vehicle (500), by use of positional information of the first vehicle (500) and positional information of the specified object. The determination unit determines performance of the first sensor (300), based on the sensing result of the specified object by the first sensor (300).

The present disclosure provides a vehicle behavior prediction method and apparatus, an electronic device, and a computer-readable storage medium, which relates to the field of intelligent driving. The method includes: determining a target traffic light corresponding to t a target vehicle a current intersection; determining a neighboring traffic light corresponding to the target traffic light and the indication status of the neighboring traffic light according to the target traffic light, the indication status of the target traffic light and a traffic light status mapping relationship table; acquiring a position of an obstacle vehicle and predicting each possible travel path of the obstacle vehicle, and determining a traffic light corresponding to each possible travel path; and determining a final possible travel path of the obstacle vehicle according to each possible travel path and the indication status of the traffic light corresponding to each possible travel path.

A vehicle control method is provided such that when a host vehicle is stopped at a front of a vehicle line of vehicles in accordance with a stop signal of a traffic light at an intersection, the engine is stopped by using an idle stop control. When either a left-turn or a right-turn will be made after the traffic light changes to a go signal, a presence or an absence of a traveling body, which is stopped on a side of or behind the host vehicle in a direction from which the host vehicle is turning, is detected during the stop signal. Upon determining the traveling body is stopped on the side of host vehicle, restarting of the engine is placed on standby even when the traffic light turns to the go signal, and the engine is restarted in accordance with a behavior of the traveling body.

This document describes techniques for localizing a vehicle to map data, for example, to enable assisted driving and automated driving within a road. A road model builder is described that uses vehicle location data to determine its position relative to map data obtained from a provider, which can be different than the location data's source. Using the vehicle position, the road model builder maintains a current lane segment for the vehicle and its relative position within the current lane segment. Based on its relative position, the road model builder determines whether a transition to another lane segment is appropriate before setting the current lane segment to be the other lane segment. The current lane segment, when provided as an input to an assisted-driving or automated-driving system, can enable safer operation of the vehicle and use fewer computing resources than other systems.