Abstract: Systems, methods and computer-readable media for spatio-temporal motion planning, including: receiving data defining a drivable area within a spatio-temporal space; generating a cost function to be minimized that includes one or more upper-bound cost terms, each upper-bound cost term approximating the upper-bound of a cost term for the trajectory for a spatial frame of the spatio-temporal space, each upper-bound cost term being expressed as one or more longitudinal-temporal cost terms and one or more lateral-temporal cost terms for the trajectory; and further cost terms for the trajectory for the spatio-temporal frames; computing, based on the constraints and cost function, a planned trajectory through the drivable area, the planned trajectory directory being optimized with respect to both spatial frame and the spatio-temporal frames.

Abstract: Systems, methods and computer-readable media for path planning for an autonomous vehicle, comprising receiving data defining an upper bound of a spatio-temporal convex corridor and lower bound of the spatio-temporal convex corridor; computing a plurality of control point times within a corridor start time and a corridor end time, the control point times including at least a start control point time, an end control point time and an intermediate control point time between the start control point time and the end control point time; determining, for each of the control point times, respective control point maximum values and control point minimum values; computing, based on the respective control point maximum values and the respective control point minimum values, respective control point values for each of the control point times, the control point values defining a curve segment that is within the upper bound and the lower bound of the spatio-temporal convex corridor.

Abstract: The present application discloses autonomous driving methods and apparatuses that may be applied to an intelligent vehicle and an autonomous driving vehicle. An example method includes: obtaining a motion status of an obstacle; determining a location of a towing point and a speed of the towing point based on the motion status of the obstacle, where both the location of the towing point and the speed of the towing point are continuous functions of the motion status of the obstacle; and obtaining expected acceleration of an ego vehicle based on the location of the towing point and the speed of the towing point.

Abstract: A trajectory planning method for an autonomous vehicle includes a lateral displacement planner that plans a preliminary traveling trajectory for an autonomous driving vehicle based on perceptual data, positioning data, and map information, where the preliminary traveling trajectory includes N road points, and each road point includes coordinate information of the road point and allowable lateral error information of the road point. A longitudinal speed planner inherits all or some of the N road points output by the lateral displacement planner and determines speed information of n road points based on traffic road condition information and the perception data to obtain a target traveling trajectory. The speed information is a drivable speed of the autonomous driving vehicle at each of the n road points. The target traveling trajectory includes the n road points, each of the n road points including the drivable speed of the autonomous driving vehicle at the road point.