Abstract: An autonomous vehicle (AV) is automatically navigated within a a driving lane. Lane line data is received from one or more sensors, such as one or more cameras. A first parametric curve can be matched to a location of a first lane line detected from the lane line data. A second parametric curve can be matched to a location of a second lane line detected from the lane line data. A center parametric curve can be then generated in the center of the first parametric curve and the second parametric curve. The center parametric curve is then sampled to generate a discrete vector of trajectory points along the center parametric curve. The AV is then navigated along the road based on the trajectory points.

Abstract: An autonomous vehicle automatically implements an intention aware lane change with a safety guaranteed lane biased strategy. To make a lane change, the autonomous vehicle first navigates within the current lane to near the edge of the current lane, to a position offset by a bias. By navigating to the edge of the lane, the autonomous vehicle provides a physical and virtual notification, in addition to a turn signal, that it is the intention of the vehicle to change into the lane adjacent to the edge of the lane in which the vehicle has navigated to. After performing a safety check and waiting for a minimum period of time during which vehicles in the adjacent lane can be assumed to have been warned or been put on notice of the lane change, the autonomous vehicle navigates into the adjacent lane.

Abstract: An autonomous vehicle automatically implements a lane change in dense traffic condition. A minimum distance gap between the vehicle and a vehicle in front of the present vehicle is calculated for an autonomous vehicle, along with a best trajectory for changing lanes into a left adjacent lane or changing lanes into a right adjacent lane. The left or right lane change is triggered by the driver or the global planner that navigates the vehicle. During the next cycle, pre-calculated information is utilized by a planning module to determine the final speed of the trajectory to complete the final planning trajectory for the lane change.

Abstract: A combined virtual and real environment for autonomous vehicle planning and control testing. An autonomous vehicle is operated in a real environment where a planning module and control module operate to plan and execute vehicle navigation. Simulated environment elements, including simulated image and video detected objects, simulated radar detected objects, simulated lane lines, and other simulated elements detectable by radar, lidar, camera, and any other vehicle perception systems, are received along with real-world detected elements. The simulated and real-world elements are combined and processed to by the autonomous vehicle data processing system. Once processed, the autonomous vehicle plans and executes navigation based on mixed real-world and simulated data in the same way. By adding simulated data to real data, the autonomous vehicle systems may be tested in hypothetical situations in a real-world environment and conditions.

Abstract: An autonomous vehicle automatically implements an intention aware lane change with a safety guaranteed lane biased strategy. To make a lane change, the autonomous vehicle first navigates within the current lane to near the edge of the current lane, to a position offset by a bias. By navigating to the edge of the lane, the autonomous vehicle provides a physical and virtual notification, in addition to a turn signal, that it is the intention of the vehicle to change into the lane adjacent to the edge of the lane in which the vehicle has navigated to. After performing a safety check and waiting for a minimum period of time during which vehicles in the adjacent lane can be assumed to have been warned or been put on notice of the lane change, the autonomous vehicle navigates into the adjacent lane.