Abstract:
A method of detecting and tracking objects using multiple radar sensors. Objects relative to a host vehicle are detected from radar data generated by a sensing device. The radar data includes Doppler measurement data. Clusters are formed, by a processor, as a function of the radar data. Each cluster represents a respective object. Each respective object is classified, by the processor, as stationary or non-stationary based on the Doppler measurement data of each object and a vehicle speed of the host vehicle. Target tracking is applied, by the processor, on an object using Doppler measurement data over time in response to the object classified as a non-stationary object; otherwise, updating an occupancy grid in response to classifying the object as a stationary object.
Abstract:
A method and system for localizing a vehicle in a digital map includes generating GPS coordinates of the vehicle on the traveled road and retrieving from a database a digital map of a region traveled by the vehicle based on the location of the GPS coordinates. The digital map includes a geographic mapping of a traveled road and registered roadside objects. The registered roadside objects are positionally identified in the digital map by longitudinal and lateral coordinates. Roadside objects in the region traveled are sensed by the vehicle. The sensed roadside objects are identified on the digital map. A vehicle position on the traveled road is determined utilizing coordinates of the sensed roadside objects identified in the digital map. The position of the vehicle is localized in the road as a function of the GPS coordinates and the determined vehicle position utilizing the coordinates of the sensed roadside objects.
Abstract:
A system and method designed to improve sensor visibility for a host vehicle operating in an autonomous driving mode when one or more forward-looking sensors are being occluded or obstructed. According to an exemplary embodiment, when a forward-looking object detection sensor is being obstructed by a target vehicle located closely ahead of the host vehicle, the method determines if lateral movement by the host vehicle within its own lane is appropriate to improve sensor visibility around the target vehicle. If lateral movement is deemed appropriate, the method generates lateral movement commands that dictate the direction and distance of the lateral movement by the host vehicle. This may enable the object detection sensors to at least partially see around the obstructing target vehicle and improve the preview distance of the sensors.
Abstract:
A method and system for localizing a vehicle in a digital map includes generating GPS coordinates of the vehicle on the traveled road and retrieving from a database a digital map of a region traveled by the vehicle based on the location of the GPS coordinates. The digital map includes a geographic mapping of a traveled road and registered roadside objects. The registered roadside objects are positionally identified in the digital map by longitudinal and lateral coordinates. Roadside objects in the region traveled are sensed by the vehicle. The sensed roadside objects are identified on the digital map. A vehicle position on the traveled road is determined utilizing coordinates of the sensed roadside objects identified in the digital map. The position of the vehicle is localized in the road as a function of the GPS coordinates and the determined vehicle position utilizing the coordinates of the sensed roadside objects.
Abstract:
A method for localizing a vehicle in a digital map. GPS raw measurement data is retrieved from satellites. A digital map of a region traveled by the vehicle based on the raw measurement data is retrieved from a database. The digital map includes a geographic mapping of a traveled road and registered roadside objects. The registered roadside objects are positionally identified in the digital map by earth-fixed coordinates. Roadside objects are sensed in the region traveled by the vehicle using distance data and bearing angle data. The sensed roadside objects are matched on the digital map. A vehicle position is determined on the traveled road by fusing raw measurement data and sensor measurements of the identified roadside objects. The position of the vehicle is represented as a function of linearizing raw measurement data and the sensor measurement data as derived by a Jacobian matrix and normalized measurements, respectively.