Trajectory optimization and state selection for urban automated driving

Abstract

The automated driving is an emerging technology in which a car performs recognition, decision making, and control. The decision-making system consists of route planning and trajectory planning. The route planning optimizes the shortest path to the destination like an automotive navigation system. According to static and dynamic obstacles around the vehicle, the trajectory planning generates lateral and longitudinal profiles for vehicle maneuver to drive the given path. This study is focused on the trajectory planning for vehicle maneuver in urban traffic scenes. This paper proposes a trajectory generation method that extends the existing method to generate more natural behavior with small acceleration and deceleration. This paper introduces an intermediate behavior to gradually switch from the velocity keeping to the distance keeping. The proposed method can generate smooth trajectory with small acceleration/deceleration. Numerical experiments show that the vehicle generates smooth behaviors according to surrounding vehicles.

Longitudinal and lateral control of autonomous vehicles in multi‐vehicle driving environments

“…In [14], a hierarchical motion planning framework was proposed, where the conjugate gradient non‐linear optimisation algorithm and the cubic B‐spline curve were employed to smooth and interpolate the reference path as well as handle both static and moving objects. The study by Yoneda et al [15] focused on the trajectory planning for vehicle manoeuvring in urban traffic scenarios. In response to static and dynamic obstacles around the vehicle, the trajectory planning generates lateral and longitudinal profiles for the vehicle to drive along the given path.…”

Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Maneuver Variants

“…Obstacles: In real conditions, the AV must maneuver among moving and stationary obstacles [13]. One of the main tasks on AV's motion-planning strategy is to prevent collisions and maintain safe distances [14]. The issue is complicated by the fact that it is difficult to accurately estimate the motion directions and speeds of other participants for a relatively long time.…”