The vehicle transport management device includes a processor configured to acquire vehicle information relating to a vehicle to be transported, and select a particular type of vehicle transport device from the plurality of types of vehicle transport devices as a vehicle transport device for transporting the vehicle based on the vehicle information.

Systems and methods for an autonomous vehicle are provided. In one aspect, an autonomous vehicle includes a perception sensor and a processor configured to: receive detected roadway conditions data including roadway grade data from the perception sensor, retrieve mapped data having grade data, and determine that the roadway has a grade based on the detected roadway grade data and the retrieved roadway grade data. The processor can be further configured to, in response to determining that the roadway has a grade, determine that the grade of the roadway is greater than or equal to a predetermined high grade value and less than a predetermined grade limit, and in response to determining that the grade of the roadway is greater than or equal to the predetermined high grade value and less than the predetermined grade limit, operate the autonomous vehicle to change lane to a right-most lane.

A smooth cooperative lane change control method for multi-connected and autonomous vehicles (CAVs), including: acquiring vehicle information of a lane-changing vehicle M and four surrounding vehicles A, B, C and D; constructing uncontrolled-vehicle and controlled-vehicle motion state prediction models; according to the motion state prediction models, predicting motion states of the lane-changing vehicle M, and the vehicles A, B, C and D; constructing an upper-layer optimization model to calculate an optimal control value and an optimized motion state of the lane-changing vehicle M and an optimal control value of the vehicle A; constructing a lower-layer optimization model to calculate an optimal control value of the vehicle D; and controlling the lane-changing vehicle M, the vehicle A and the vehicle D to run according to a corresponding optimal control value.

An approach is provided for determining an autonomous vehicle reaction strategy when facing a wrong way driving vehicle. The approach, for example, involves processing mapping data, sensor data, or a combination thereof to develop a map of an area within a threshold distance of an autonomous vehicle in response to a detection of an oncoming vehicle that is driving in a wrong direction towards the autonomous vehicle. The approach also involves using a decision tree of a plurality of candidate strategies for avoiding the oncoming vehicle to select a strategy based on one or more attributes of the map. The approach further involves presenting the selected strategy in a user interface of the autonomous vehicle.

A control apparatus of a vehicle includes: a steering apparatus (6) including a steering wheel (11) operated in order to turn a vehicle (1) and a steering angle sensor (8) that detects a steering angle of the steering wheel (11), the steering apparatus (6) steering a front wheel (steered wheel) (2) of the vehicle (1) in accordance with operation of the steering wheel (11); and a controller (14) that sets a steering angle acceleration based on the steering angle detected by the steering angle sensor (8) and controls vehicle motion when the steering wheel (11) is operated to be turned. In particular, the controller (14) suppresses a rise of lateral acceleration of the vehicle (1) based on the steering angle acceleration in order to control the vehicle motion.

Examples provide for computer executable instructions executable by a processor to configure the processor to determine an occupancy status of cells of a region of interest by, for a first cell of the region of interest, determining whether the first cell is free by setting a free space flag for the first cell to an asserted value and setting a free space tested flag for the first cell to an asserted value responsive to determining that the first cell and each of the surrounding cells in the programmed radius of the first cell are unoccupied, and setting a free space flag for a second cell to a de-asserted value and setting a free space tested flag for the second cell to an asserted value responsive to determining that the second cell is occupied, the second cell being among the surrounding cells in the programmed radius of the first cell.

Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode or an autonomous vehicle. For instance, a polygon representative of the shape and location of a first object may be received. A polyline contour representation of a portion of a polygon representative of the shape and location of a second object may be received. The polyline contour representation may be in half-plane coordinates and including a plurality of vertices and line segments. Coordinates of the polygon representative of the first object may be converted to the half-plane coordinate system. A collision location between the polyline contour representation and the polygon representative of the first object may be determined using the converted coordinates. The autonomous vehicle may be controlled in the autonomous driving mode to avoid a collision based on the collision location.

A vehicle includes an inputter receiving an emergency stop command; an emergency stop condition determiner determining that an emergency stop condition is satisfied when a driver's state is determined as a predetermined inoperable state, a steering wheel is not operated for a predetermined time period, or a rate of change of a yaw rate of the vehicle exceeds a predetermined value; a sensor configured for detecting an obstacle around the vehicle; and a controller configured to determine whether the vehicle can avoid collision with a front obstacle only by braking without a lane change, to determine a risk area in an adjacent lane based on a braking distance of the vehicle and obstacle detection information in a lane adjacent to a driving lane of the vehicle, and to control the lane change or a braking process of the vehicle based on whether the obstacle is detected in the determined risk area.

A method estimates tire pressure of vehicle. For each tire, signals or data indicative of angular velocity of the wheel with which the tire is associated are acquired. A subset of detected signals or data acquired in rectilinear vehicle travel condition is selected. Pressure relationship between tires of each pair of wheels of the same axle is determined by comparing the rolling radius of the wheel on which a first tire is mounted and the rolling radius of the wheel on which a second tire is mounted. A pressure relationship between tire pairs is determined for comparison between the mean value of the rolling radii of wheels of a first axle and the mean value of the rolling radii of wheels of a second axle. Ratios are calculated based on signals or data indicative of angular velocity of the wheels and on slippage of the drive wheels.

A system for predicting a collision risk in lane change decision based on a radar sensor, includes radar sensors disposed at a front portion and a rear portion of a host vehicle to recognize a forward vehicle positioned at a front-side portion of the host vehicle and a rearward vehicle positioned at a rear-side portion of the host vehicle, respectively, and a moving controller configured to determine that the host vehicle is able to change a lane, when a position of a counterpart vehicle, which is measured through the radar sensor, is not included in a section of the local map, and when a relative acceleration of the counterpart vehicle is maintained in an allowance range for a specific time.