An Optimization Approach to Trajectory Generation for Autonomous Vehicle Following

We present a novel approach to trajectory generation that enables an autonomous vehicle to accurately follow a lead vehicle tracked by on-board sensors. In contrast to other approaches, we ignore the structure of the environment (e.g., lane markings), focusing purely on following the path driven by the vehicle ahead. Based on the leader's velocity, its distance to the ego vehicle and its recorded path, a continuous-curvature trajectory is generated using Sequential Quadratic Programming. As the optimization process takes the ego vehicle's kinematic and dynamic constraints into account, the resulting trajectory is guaranteed to be feasible and safe. The algorithm was tested extensively during practical experiments with an actual autonomous car. Our tests were conducted in both on-and off-road environments, with speeds ranging from 1 m/s to 15 m/s. Ground truth data shows that the system achieves a high degree of accuracy even in difficult scenarios.