A vehicle drive-assist apparatus includes: a first front-environment recognizer recognizing, with a vehicle-mounted autonomous sensor, a front environment; a second front-environment recognizer recognizing the front environment on the basis of information received from an external device via external communication; a first brake controller determining, when a preceding vehicle is recognized by the first front-environment recognizer, a target position on the basis of the preceding vehicle, and executing a first brake control based on the target position; and a second brake controller determining, when the preceding vehicle is recognized only by the second front-environment recognizer, the target position on the basis of the preceding vehicle, and determining a corrected target position by correcting the target position, and executing a second brake control based on the corrected target position in advance of the first brake control. The corrected target position is farther from the own vehicle than the target position is.

The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a motion planning system that solves gridlock as part of determining a motion plan for an autonomous vehicle (AV). In particular, a scenario generator within a motion planning system can determine one or more keep clear areas associated with the lane sequence, each keep clear area indicative of a region along the nominal path in which gridlock prevention is desired. A gridlock constraint can be generated for each of the one or more keep clear areas, each constraint being defined as a constraint area in a multi-dimensional space. A low-cost trajectory path can be determined through a portion of the multi-dimensional space that minimizes exposure to the constraint areas and that is consistent with all constraints generated for the one or more objects of interest and the one or more keep clear areas.

Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes obtaining sensor data indicative of a surrounding environment of the autonomous vehicle, the surrounding environment including one or more occluded sensor zones. The method includes determining that a first occluded sensor zone of the occluded sensor zone(s) is occupied based at least in part on the sensor data. The method includes, in response to determining that the first occluded sensor zone is occupied, controlling the autonomous vehicle to travel clear of the first occluded sensor zone.

An autonomous driving system includes a lane change control device that performs lane change control for making a lane change from a first lane to a second lane during autonomous driving of a vehicle. The lane change control device is configured to: determine, from start to completion of the lane change control, whether or not a driver's operation is performed as an abort request operation that requests to abort the lane change control; when the abort request operation is performed, determine whether or not an abort permission condition is satisfied; when the abort permission condition is not satisfied, continue the lane change control; and, when the abort permission condition is satisfied, abort the lane change control and make the vehicle travel in the first lane. The lane change control device changes ease of satisfaction of the abort permission condition depending on the situation.

A dynamic velocity planning method for an autonomous vehicle is performed to plan a best velocity curve of the autonomous vehicle. An information storing step is performed to store an obstacle information, a road information and a vehicle information. An acceleration limit calculating step is performed to calculate the vehicle information according to a calculating procedure to generate an acceleration limit value range. An acceleration combination generating step is performed to generate a plurality of acceleration combinations according to the obstacle information, the road information, and the acceleration limit value range. An acceleration filtering step is performed to filter the acceleration combinations according to a jerk threshold and a jerk switching frequency threshold to obtain a selected acceleration combination. An acceleration smoothing step is performed to execute a driving behavior procedure to adjust the selected acceleration combination to generate the best velocity curve.

Systems, methods, and devices are disclosed for predicting behaviors of objects (vehicles, bicycles, pedestrians, etc.) at a location. A model descriptive of a possible object behavior can be received by an autonomous vehicle, where the model provides conditional predictions about a future behavior of an object based on a position of the object in a lane. The autonomous vehicle can detect the position of a specific object in the lane, and the model can then be applied to determine probabilities of a future behavior of the specific object.

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.

A sign detection device includes an information acquiring unit to acquire eye opening degree information indicating an eye opening degree of a driver in a mobile object, surrounding information indicating a surrounding state of the mobile object, and mobile object information indicating a state of the mobile object, and a sign detection unit to detect a sign of the driver dozing off by determining whether the eye opening degree satisfies a first condition based on a threshold and by determining whether a state of the mobile object satisfies a second condition corresponding to the surrounding state.

Methods and systems that use a phantom vehicle to help generate a planned path for a real-world vehicle are described. The system will identify a starting point and a destination for a trip of the real-world vehicle. The system will select, from the data store of vehicle profiles, a phantom vehicle having an associated motion planning system that corresponds to a system that is deployed on the real-world vehicle. The system will use a high definition map to generate a planned route for the real-world vehicle from the starting point to the destination in the map. The system will run a simulation in which the phantom vehicle moves along the planned route in the map. The system will then output a record of the simulation to a user of the real world-vehicle or to a system of the real-world vehicle.

The disclosure relates to an automated lane changing method for a vehicle. The method includes: obtaining a plurality of areas in a target lane; calculating, based on information about an obstacle included in each area in the plurality of areas, an expected lane changing point for the area; and selecting, based on the expected lane changing point and an availability check result for the plurality of areas, a first area in the plurality of areas as a target area for final lane changing. The disclosure further relates to an automated lane changing device for a vehicle, a computer storage medium, and a vehicle.