Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).

This document describes techniques for using occlusion constraints for resolving tracks from multiple types of sensors. In aspects, an occlusion constraint is applied to an association between a radar track and vision track to indicate a probability of occlusion. In other aspects, described are techniques for a vehicle to refrain from evaluating occluded radar tracks and vision tracks collected by a perception system. The probability of occlusion is utilized for deemphasizing pairs of radar tracks and vision tracks with a high likelihood of occlusion and therefore, not useful for tracking. The disclosed techniques may provide improved perception data more closely representing multiple complex data sets for a vehicle for preventing a collision with an occluded object as the vehicle operates in an environment.

While operating a host vehicle in a lane, a target vehicle is detected entering the lane in front of the vehicle. A trajectory of the target vehicle is predicted based on sensor data. Upon determining that the target vehicle will pass through the lane based on the predicted trajectory, the host vehicle is operated based on determining a presence or an absence of a lead vehicle. Upon determining that the target vehicle will remain in the lane based on the predicted trajectory, the host vehicle is operated with the target vehicle as the lead vehicle.

An information processing system, including: a surveillance camera that detects a plurality of obstacles in the vicinity of a specific vehicle; a first determiner that determines whether an unidentified obstacle, which is included in the plurality of obstacles and is not visible from the specific vehicle, is present based on first information regarding the plurality of obstacles detected by the surveillance camera and vehicle information indicating the specific vehicle; and a first communicator that outputs information indicating the unidentified obstacle to the specific vehicle when the first determiner determines that the unidentified obstacle is present.

The present disclosure relates to an apparatus and a method for controlling a vehicle, and, particularly, an apparatus and a method for controlling a vehicle capable of preventing a collision accident in advance by expanding a detection area of a vehicle by monitoring a lower end of a parked/stopped vehicle by adjusting a rotation angle of a sensor attached to the lower end of the vehicle on a front side and predicting a situation having a risk for a collision that may occur due to a blind spot in advance can be provided.

An information processing apparatus includes: a point group data acquisition unit configured to acquire, based on information from a sensor configured to detect an object existing in surroundings of a vehicle, point group data related to a plurality of points representing the object; a movement amount estimation unit configured to estimate a movement amount of the vehicle; a storage unit configured to store, as a point group map recorded in association with position information including a latitude and a longitude, relative positions of the plurality of points relative to a first reference position that is a place on a travel path of the vehicle; and a position estimation unit configured to estimate a position of the vehicle based on the point group map, the point group data, and the movement amount.

An autonomous vehicle can obtain sensor data. Upon determining that the autonomous vehicle is in a lane adjacent a shoulder, and there is an object in the shoulder, the autonomous vehicle can determine if performing a lane change maneuver out of the lane prior to the autonomous vehicle being positioned adjacent to the object is feasible. If it is, the lane change maneuver can be performed. If it is not, a nudge maneuver and/or a deceleration can be performed.

A method for determining a lane change, performed by an apparatus for determining a lane change of an object located around a driving vehicle with which is equipped a sensor, the method including, detecting a plurality of objects located around the driving vehicle using scanning information obtained repeatedly at every predetermined period of time by the sensor scanning surroundings of the driving vehicle, selecting at least one candidate object estimated to change lanes among the plurality of objects based on previously detected lane edge information and determining whether the candidate object changes lanes based on information on movement of the candidate object.

The present disclosure relates to an autonomous vehicle system or a driver assistance system that detects an object in front of the vehicle, judges whether a condition calls for an operation of a lateral movement system for collision avoidance, determines a direction of the lateral movement, and executes the lateral movement.

A system of linearizing a trajectory of an autonomous vehicle about a reference path includes a computing device and a computer-readable storage medium. The computer-readable storage medium includes one or more programming instructions that, when executed, cause the computing device to receive a reference path for an autonomous vehicle, where the reference path defines a proposed trajectory for the autonomous vehicle in a Cartesian reference frame, identify an objective based on the received reference path, where the objective comprises a longitudinal component and a lateral component, project the objective into a curvilinear coordinate frame described by the received reference path, decouple the longitudinal component and the lateral component, linearize the lateral component about the reference path, generate a new reference path for the autonomous vehicle by fusing the linearized longitudinal component and the linearized lateral component, and map the new reference path back to the Cartesian reference frame.