The present invention is a system and method for stopped vehicle driver alert. The system gives alert to the driver at traffic light when a vehicle is stopped at red light, when the light changes from red to green, and/or when the front vehicle clears out moving away above a threshold distance. In preferred embodiments, system comprises optical sensors to detect traffic lights and changes of traffic light status, object detection and object distance measurement sensors, GPS location sensors, a microprocessor with memory, engine control module, and alert systems capable of playing or displaying alerts of audible, visual, and haptic formats. The system is also capable of generating engine start signal. The method detects if the ego vehicle is stopped due to a traffic light and/or traffic jam or a scenario where the front vehicle is almost stopped or within a threshold distance from the ego vehicle. When the condition changes. If the traffic light is red the method determines how long the red light will exist before changing to green with the help of precalculated table where red light duration of a particular GPS location on a particular road is given. The method also calculates a complete red light duration of it can detect the start of the red light and update the red light duration. The method generates remaining light duration signal and send the signal to audible or visual alert systems. Right before or after the remaining time turns to zero, the method generates audible, visual, and/or haptic signals and sends the signals to the audible, visual, and haptic alert systems and/or sends engine start signal to engine control module. The method also determines if red light changed to green and sends audible, visual, and/or haptic signals and/or engine start signal to engine control module.

Image data are input to a machine learning program. The machine learning program is trained with a virtual boundary model based on a distance between a host vehicle and a target object and a loss function based on a real-world physical model. An identification of a threat object is output from the machine learning program. A subsystem of the host vehicle is actuated based on the identification of the threat object.

Provided is an apparatus for assisting travelling of a vehicle, the apparatus including: a camera disposed in the vehicle, having a field of view of a front of the vehicle, and acquiring image data; and a controller including a processor for processing the image data, wherein the controller is configured to: identify a lane being travelled on by the vehicle and a preceding vehicle travelling in front of the vehicle based on the image data processed, and in response to the preceding vehicle crossing a center line, generate a control signal for controlling the vehicle based on the preceding vehicle crossing the center line.

Embodiments relate to a method, an apparatus, a computer program, a base station and a vehicle for providing information related to an approaching vehicle. The method comprises Receiving (110) information related to a velocity of a plurality of vehicles. The method further comprises Determining (120) the information related to the approaching vehicle based on the information related to the velocity of the plurality of vehicles. The information related to the approaching vehicle indicates a presence of a vehicle of the plurality of vehicles having a velocity higher than an average velocity of the plurality of vehicles. The method further comprises Providing (130) the information related to the approaching vehicle to at least a subset of vehicles of the plurality of vehicles.

A driver assistance system that notifies the driver of the braking information of the preceding vehicle or improves the sensitivity of the emergency braking system if the brake lamp of the preceding vehicle is failed includes: a first sensor mounted in a vehicle, having a front field of view of the vehicle, and configured to acquire a front image data; a second sensor selected from a group consisting of a radar sensor and a lidar sensor, mounted in the vehicle, having a front field of sensing of the vehicle, and configured to acquire a front detection data; and a controller including a processor configured to process the front image data and the front detection data, and the controller is configured to: detect a velocity of the preceding vehicle travelling in front of the vehicle and a brake lamp of the preceding vehicle in response to processing the front image data and the front detection data; and determine whether the brake lamp of the preceding vehicle is failed based on the velocity of the preceding vehicle and a lighting of the brake lamp of the preceding vehicle.

The disclosure provides an environment perception device and method of a mobile vehicle. The environment perception device includes a camera module, a LiDAR module, a database and a processing circuit. The camera module photographs a field near the mobile vehicle to generate a three-dimensional (3D) image frame. The LiDAR module scans the field to generate a 3D scanned frame. The processing circuit fuses the 3D image frame and the 3D scanned frame to generate 3D object information. The processing circuit compares the 3D object information with a 3D map in the database to determine whether an object is a static object. The processing circuit performs an analysis and calculation on the 3D object information to obtain movement characteristics of the object when the object is not the static object, and skips the analysis and calculation on the 3D object information when the object is the static object.

A device of the present disclosure controls a vehicle speed of a vehicle such that the vehicle speed matches a set vehicle speed when a preceding vehicle is not detected, and controls the vehicle speed of the vehicle such that the vehicle follows the preceding vehicle when the preceding vehicle is detected. The device controls the vehicle speed of the vehicle assuming that the preceding vehicle is not detected, when a condition that allows the vehicle to overtake the two-wheeled vehicle is satisfied and an intention of a driver to overtake a two-wheeled vehicle is detected, in a case where the two-wheeled vehicle is detected as the preceding vehicle and the vehicle speed is controlled such that the vehicle follows the two-wheeled vehicle.

A LIDAR system includes a plurality of LIDAR units. Each of the LIDAR units includes a housing defining a cavity. Each of the LIDAR units further includes a plurality of emitters disposed within the cavity. Each of the plurality of emitters is configured to emit a laser beam. The LIDAR system includes a rotating mirror and a retarder. The retarder is configurable in at least a first mode and a second mode to control a polarization state of a plurality of laser beams emitted from each of the plurality of LIDAR units. The LIDAR system includes a polarizing beam splitter positioned relative to the retarder such that the polarizing beam splitter receives a plurality of laser beams exiting the retarder. The polarizing beam is configured to transmit or reflect the plurality of laser beams exiting the retarder based on the polarization state of the laser beams exiting the retarder.

A method of moving an autonomous vehicle to a safe zone after an accident is provided. The method includes: setting a threshold which is a criterion for determining a failure of a chassis system; determining whether a failure occurs by using the threshold; determining a control mode of twin clutches or a braking system; designating avoidance speed level in accordance with whether a following vehicle approaches; and setting a target trajectory to a safe zone and then generating braking torque on left and right wheels or controlling distribution of driving torque through the twin clutches in order to move the autonomous vehicle at the avoidance speed along the target trajectory.

An autonomous vehicle computing system can include a primary perception system configured to receive a plurality of sensor data points as input generate primary perception data representing a plurality of classifiable objects and a plurality of paths representing tracked motion of the plurality of classifiable objects. The autonomous vehicle computing system can include a secondary perception system configured to receive the plurality of sensor data points as input, cluster a subset of the plurality of sensor data points of the sensor data to generate one or more sensor data point clusters representing one or more unclassifiable objects that are not classifiable by the primary perception system, and generate secondary path data representing tracked motion of the one or more unclassifiable objects. The autonomous vehicle computing system can generate fused perception data based on the primary perception data and the one or more unclassifiable objects.