An operation-security system for an automated vehicle includes an object-detector and a controller. The object-detector includes at least three sensors. Each sensor is one of a camera used to determine an image-location of an object proximate to a host-vehicle, a lidar-unit used to determine a lidar-location of the object proximate to the host-vehicle, and a radar-unit used to determine a radar-location of the object proximate to the host-vehicle. The controller is in communication with the at least three sensors. The controller is configured to determine a composite-location based on a comparison of locations indicated by the at least three sensors. Information from one sensor is ignored when a respective location indicated by the one sensor differs from the composite-location by greater than an error-threshold. If a remote sensor not on the host-vehicle is used, V2V or V2I communications may be used to communicate a location to the host-vehicle.

A hybrid electric vehicle and a method of controlling the same are provided to avoid a collision thereof attributable to erroneous operation of an accelerator pedal. The method includes determining whether an accelerator pedal is erroneously operated in the situation in which an obstacle is detected to be present in the traveling path. In response to determining that the accelerator pedal is erroneously operated, the method includes switching the driving mode to a mode in which an engine is disconnected from a driving shaft and a motor generates driving force. The number of revolutions per minute (RPM) of the engine is then adjusted based on the extent to which the accelerator pedal is operated and the torque of the motor is adjusted based on a first vehicle speed and the distance to the obstacle.

In a system trigger mode and a driver trigger mode, when a start condition for an automated lane change function by autonomous travel control is satisfied, lane change information as to whether to accept execution of the automated lane change function is presented; when an acceptance input of accepting the execution is detected, a determination is made as to whether the lane change is possible; and the automated lane change function is executed when it is determined possible. In the driver trigger mode, when a predetermined lane change instruction operation is performed, a determination is made as to whether the lane change is possible, and when a determination is made that lane change is possible, the automated lane change function is executed. The time for the lane change determination by the driver trigger mode is set shorter than the time for the lane change determination by the system trigger mode.

Methods, systems, and devices for ranging are described. A multi-phase distributed ranging technique includes transmitting and receiving vehicle information messages during a first time interval, where the vehicle information messages include at least a vehicle identifier and resource information. The multi-phase technique further includes transmitting and receiving ranging signals during a second time interval, and determining times of arrival of received ranging signals. A centralized ranging technique includes receiving resource assignments from an access point, transmitting ranging signals according to the resource assignments, and determining times of arrival of received ranging signals.

Vehicles and methods for tracking an object and controlling a vehicle based on the tracked object. A Radar-Doppler (RD) map is received from the radar sensing system of the vehicle and relative acceleration of an object with respect to the vehicle is detected based on the RD map so as to provide acceleration data. A current frame of detected object data is received from a sensing system of the vehicle. When the relative acceleration has been detected, a tracking algorithm is adapted to reduce the influence of the predictive motion model or the historical state of the object and the object is tracked using the adapted tracking algorithm so as to provide adapted estimated object data based on the object tracking. One or more vehicle actuators are controlled based on the adapted estimated object data.

A method includes accessing a training sample including an image of a scene, depth measurements of the scene, and a predetermined 3D position of an object in the scene. The method includes training a 3D-detection model for detecting 3D positions of objects based the depth measurements and the predetermined 3D position, and training a 2D-detection model for detecting 2D positions of objects within images. Training the 2D-detection model includes generating an estimated 2D position of the object by processing the image using the 2D-detection model, determining a subset of the depth measurements that correspond to the object based on the estimated 2D position and a viewpoint from which the image is captured, generating an estimated 3D position of the object based on the subset of the depth measurements, and updating the 2D-detection model based on a comparison between the estimated 3D position and the predetermined 3D position.

A variety of methods, controllers and algorithms are described for identifying the back of a particular vehicle (e.g., a platoon partner) in a set of distance measurement scenes and/or for tracking the back of such a vehicle. The described techniques can be used in conjunction with a variety of different distance measuring technologies including radar, LIDAR, camera based distance measuring units and others. The described approaches are well suited for use in vehicle platooning and/or vehicle convoying systems including tractor-trailer truck platooning applications. In another aspect, technique are described for fusing sensor data obtained from different vehicles for use in the at least partial automatic control of a particular vehicle. The described techniques are well suited for use in conjunction with a variety of different vehicle control applications including platooning, convoying and other connected driving applications including tractor-trailer truck platooning applications.

A traveling environment recognition apparatus includes a first detector that detects an object in a first region outside a vehicle, a second detector that detects an object in a second search region that at least partly overlaps with the first region, a determiner that determines whether two objects respectively detected by the two detectors are a same object, in an overlapping region of the search regions; and a recognizer that integrates the detected objects, and to recognize the detected objects as one fusion object. The recognizer compares a threshold with a parameter based on distances from the vehicle to the detected objects, to recognize the fusion object using a worst value of detection results of the detected objects when the detected objects are near the vehicle, and recognize the fusion object using a mean value of the detection results when the detected objects are far from the vehicle.

A system for providing smart road infrastructure for the purpose of vehicle safety and autonomous driving, comprising a plurality of road units, which are located along the borders of each traffic lane and equally spaced from each other, where each road unit includes a read/write passive RF tag; antenna for communicating with a plurality of transceivers, each of which is installed on each vehicle that travels along a traffic lane of said road, in response to signals transmitted from said transceivers; a memory for temporarily storing data regarding each vehicle traveling along said lane. Each car unit comprises a reader for interrogating said tags. The reader includes a first transceiver that is installed on the left front of said vehicle and a second transceiver that is installed on the right front of said vehicle; a processor being in bidirectional data communication with said transceivers and with the vehicle inherent control systems, for processing data received from said tags and calculating speed and location of said vehicle with respect to the borders of said lane and to other neighboring vehicles traveling in said lane and adjacent lanes, to implement vehicle safety operations such as Lane Departure Warning, Forward Collision Warning, Lane Keeping Assist, Lane Centering, Side Collision Warning. Alerting the driver (visually and/or audibly) regarding potential problems and/or taking over control of the vehicle (ADAS 1-5). The system can provide Connected Vehicles with accurate (ubiquitous and instantaneous) location data with lane-level resolution. The proposed smart infrastructure may complement car sensors and/or connected vehicles, so as to implement a combination that yield the most relabel and cost-effective autonomous driving system.

An operation-security system for an automated vehicle includes an object-detector and a controller. The object-detector includes at least three sensors. Each sensor is one of a camera used to determine an image-location of an object proximate to a host-vehicle, a lidar-unit used to determine a lidar-location of the object proximate to the host-vehicle, and a radar-unit used to determine a radar-location of the object proximate to the host-vehicle. The controller is in communication with the at least three sensors. The controller is configured to determine a composite-location based on a comparison of locations indicated by the at least three sensors. Information from one sensor is ignored when a respective location indicated by the one sensor differs from the composite-location by greater than an error-threshold. If a remote sensor not on the host-vehicle is used, V2V or V2I communications may be used to communicate a location to the host-vehicle.