Abstract: An aspect of the present disclosure relates to a system implemented in a host vehicle, comprising: a radar based detection unit, comprising one or more radar sensors, for detecting one or more targets around the host vehicle; a vision based detection unit, comprising one or more image sensors, for detecting one or more targets in the field of view of the host vehicle; and a processing unit to: receive information corresponding to detected one or more targets from each of the radar based detection unit and the vision based detection unit; match each of the one or more targets detected by the radar based detection unit with one or more targets detected by vision based detection unit to identify a target as a matched target; categorize the matched target as a locked target; and track the locked target using information received from the radar based detection unit.

Abstract: A prediction system implemented in a host vehicle to predict a merge cut-in for an autonomous vehicle. The system comprises an input unit for capturing neighboring information of the host vehicle, and a processing unit to receive the captured neighboring information and generate a grid map by determining shape and dimensions of a grid, estimate trajectory of each target vehicle of the one or more target vehicles, based on a driver behavior model of each target vehicle, to determine optimized path of each target vehicle, and generate a global maneuver model by analyzing motion of each neighboring target vehicle, wherein on generation of the global maneuver model a merge cut-in threat for the host vehicle is computed by performing centralized risk management and utilizing the predicted trajectory of the one or more target vehicles.

Abstract: An aspect of the present disclosure relates to a system implemented in a host vehicle, comprising: a radar based detection unit, comprising one or more radar sensors, for detecting one or more targets around the host vehicle; a vision based detection unit, comprising one or more image sensors, for detecting one or more targets in the field of view of the host vehicle; and a processing unit to: receive information corresponding to detected one or more targets from each of the radar based detection unit and the vision based detection unit; match each of the one or more targets detected by the radar based detection unit with one or more targets detected by vision based detection unit to identify a target as a matched target; categorize the matched target as a locked target; and track the locked target using information received from the radar based detection unit.

Abstract: The pedestrian tracking system implemented in a host vehicle is disclosed. The system estimates trajectories of the host vehicle and the pedestrian based on factors of position and velocity of the host vehicle and parameters of position and velocity of the pedestrian. The trajectories of the host vehicle and the pedestrian are estimated to estimate a point of intersection of the host vehicle and the pedestrian. Further, the system estimates time to collision based on the estimated point of intersection and determines trajectory of the pedestrian in path of the host vehicle. Furthermore, the system assesses collision risk to select the pedestrian as a target and generates a deceleration actuation command to decelerate the host vehicle based on the selected target, which is provided to an automatic emergency braking (AEB) actuation unit.

Abstract: A system implemented in a vehicle for tracking and mapping of one or more objects to identify free space is disclosed. The system has an input unit having lidar sensors and radar sensors that sense objects in a region surrounding the vehicle, and a processing unit that: receives data from lidar sensors and radar sensors and maps the data in corresponding grid maps of corresponding sensors; tracks objects in regions corresponding to the sensors and performs estimation for objects not sensed by any of the sensors; fuses the grid maps by converting them from sensor frame to vehicle frame to generate a fused grid map; and integrates the fused grid map with any or a combination of track management and scan matching to perform classification of the one or more objects into static objects or dynamic objects and identification of free space in the fused grid map.

Abstract: A prediction system implemented in a host vehicle to predict a merge cut-in for an autonomous vehicle. The system comprises an input unit for capturing neighboring information of the host vehicle, and a processing unit to receive the captured neighboring information and generate a grid map by determining shape and dimensions of a grid, estimate trajectory of each target vehicle of the one or more target vehicles, based on a driver behavior model of each target vehicle, to determine optimized path of each target vehicle, and generate a global maneuver model by analyzing motion of each neighboring target vehicle, wherein on generation of the global maneuver model a merge cut-in threat for the host vehicle is computed by performing centralized risk management and utilizing the predicted trajectory of the one or more target vehicles.

Abstract: The present invention is an induction control system for an autonomous-traveling vehicle that performs traveling and work autonomously while measuring the position of the traveling vehicle by using a satellite positioning system, the induction control system being characterized by, when the autonomous-traveling vehicle is moved backward, setting a virtual antenna position more backward, by a predetermined distance, than the antenna position of the satellite positioning system, and performing a lateral control by using a lateral deviation from a target path at the virtual antenna position. This makes it possible to travel along a pre-designed target path during a forward movement, turning, and a backward movement of the autonomous-traveling vehicle.

Abstract: The present invention is an induction control system for an autonomous-traveling vehicle that performs traveling and work autonomously while measuring the position of the traveling vehicle by using a satellite positioning system, the induction control system being characterized by, when the autonomous-traveling vehicle is moved backward, setting a virtual antenna position more backward, by a predetermined distance, than the antenna position of the satellite positioning system, and performing a lateral control by using a lateral deviation from a target path at the virtual antenna position. This makes it possible to travel along a pre-designed target path during a forward movement, turning, and a backward movement of the autonomous-traveling vehicle.