A computer includes a processor and a memory storing instructions executable by the processor to receive sensor data indicating a current position of an object, determine a predicted position of the object at a future time, and instruct a component of a vehicle to actuate based on the current position being in a first zone of a plurality of zones surrounding the vehicle and the predicted position being in a second zone of the plurality of zones different than the first zone. The zones are nonoverlapping and have preset boundaries relative to the vehicle.

A method for safe at least semi-autonomous driving operation of an ego vehicle in case of sun glare is disclosed. The method involves checking, by a computing system, whether one or more vehicle sensors of the ego vehicle are dazzled by sun glare, and if yes, detecting environmental information by a detection system of the ego vehicle. The method further involves subsequently checking, by a computing system, the environmental information for a presence of at least one dynamic object for intercepting the sun glare during driving operation of the ego vehicle, and if yes, checking, by a computing system, whether the ego vehicle can execute a driving manoeuvre in such a way that the at least one dynamic object intercepts the sun glare during driving operation of the ego vehicle. If yes, then the driving manoeuvre is executed.

A vehicle includes a radar mounted to have a front field of view and a lateral field of view of the vehicle and configured to detect an object and acquire object data; a sensor configured to detect a movement of the vehicle and acquire motion data based on the movement of the vehicle; and a controller comprising a processor configured to process the object data and the motion data.

A vehicle includes a controller programed to: collect a set of data related to a driver of the vehicle; predict a driving setting for the driver using the set of data and an initial student-T process (STP) machine learning (ML) model; generate an updated STP ML model based on the prediction of the driving setting as to the set of vehicle data; transmit incremental learning related to the updated STP ML model to a server; and receive, from the server, a personalized driving setting for the driver output from a cloud STP ML model trained by the incremental learning.

The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps. The guidance system may identify a path based at least in part on determining set of nodes and a cost map associated with the static and/or dynamic object, among other costs, pruning the set of nodes, and creating further nodes from the remaining nodes until a computational or other limit is reached. The path output by the techniques may be associated with a cheapest node of the sets of nodes that were generated.

A detection method and a device based on a laser radar, and a computer readable storage medium are disclosed. The detection method includes: obtaining scanning data of the laser radar (S101); performing algorithm splitting on a feature algorithm for detection based on the scanning data to obtain at least one sub-algorithm capable of parallel processing in the feature algorithm (S102); and performing heterogeneous acceleration for the at least one sub-algorithm to process the scanning data and to obtain a processing result; and obtaining a detected position of an obstacle and a detected drivable area based on the processing result (S103).

Methods and systems for improving fuel economy and reducing emissions of a vehicle with an electric motor, an engine, an energy storage device, and a controller are disclosed. The method includes obtaining current state information including a current hybrid control surface, and determining a target hybrid control surface for the vehicle based on the current state information.

A braking control system includes obstacle detection means for detecting an obstacle ahead of a vehicle, first collision determination means for determining whether the vehicle would collide with the obstacle ahead of the vehicle, following vehicle detection means for detecting a following vehicle traveling behind the vehicle, information acquisition means for acquiring a maximum deceleration set in the following vehicle, second collision determination means for determining whether the following vehicle would collide with the vehicle based on the maximum deceleration, and braking control means for controlling braking means of the vehicle so that an absolute value of a deceleration of the vehicle does not exceed an absolute value of the maximum deceleration of the following vehicle when the first collision determination means determines that the vehicle would collide with the obstacle and the second collision determination means determines that the following vehicle would collide with the vehicle.

A computer-implemented method of determining control actions for controlling a mobile robot comprises: receiving a set of scenario description parameters describing a scenario and a desired goal for the mobile robot therein; in a first constrained optimization stage, applying a first optimizer to determine a first series of control actions that substantially globally optimizes a preliminary cost function for the scenario, the preliminary cost function based on a first computed trajectory of the mobile robot, as computed by applying a preliminary robot dynamics model to the first series of control actions, and in a second constrained optimization stage, applying a second optimizer to determine a second series of control actions that substantially globally optimizes a full cost function for the scenario, the full cost function based on a second computed trajectory of the mobile robot, as computed by applying a full robot dynamics model to the second series of control actions; wherein initialization data of at least one of the first computed trajectory and the first series of control actions is used to initialize the second optimizer for determining the second series of control actions, and wherein the preliminary robot dynamic model approximates the full robot dynamics model, the cost functions embody similar objectives to each encourage achievement of the desired goal, and both are optimized with respect to similar hard constraints, such that the initialization data guides the second optimizer to the substantially globally-optimal second series of control actions.

Provided are autonomous vehicles (AV), computer program products, and methods for maneuvering an AV in a roadway, including receiving forecast information associated with predicted trajectories of one or more actors in a roadway, determining a relevant trajectory of an actor based on correlating a forecast for predicted trajectories of the actor with the trajectory of the AV, regenerate a distance table for the relevant trajectory previously generated for processing constraints, generate a plurality of margins for the AV to evaluate, the margins based on a plurality of margin types for providing information about risks and effects on passenger comfort associated with a future proximity of the AV to the actor, classifying an interaction between the AV and the actor based on a plurality of margins, and generating continuous scores for each candidate trajectory that is also within the margin of the actor generated for the relevant trajectory.