A Dynamic Motion Planning Framework for Autonomous Driving in Urban Environments

TL;DR

This paper introduces a comprehensive motion planning framework for autonomous vehicles in urban settings, combining trajectory smoothing, human-like path generation, obstacle avoidance, and control strategies validated through simulation and real-world deployment.

Contribution

It presents a novel integrated framework that enhances trajectory generation and control for autonomous driving in complex urban environments.

Findings

Successfully tested on a vehicle that traveled thousands of miles in urban areas.

Generated trajectories that are smooth, human-like, and kinematically feasible.

Framework outperforms traditional methods in safety and comfort metrics.

Abstract

Abstract: we present a framework for robust autonomous driving motion planning system in urban environments which includes trajectory refinement, trajectory interpolation, avoidance of static and dynamic obstacles, and trajectory tracking. Given road centerline, our approach smoother the original line via cubic spline. Fifth order Bezier curve is then used to generate more human-like trajectories that guarantee at least second order continuity and curvature continuity. Dynamic trajectory planning task is decoupled into lateral spatial and longitudinal velocity planning problems. A bunch of candidate trajectory sets are generated and evaluated by an object function which considers kinematic feasibility, trajectory smoothness, driving comfort and collision-checking. Meanwhile, an LQG controller is used to generate longitudinal velocity profile to ensure safety and comfort. After that, spatial and velocity profiles are transformed into commands executed by lateral steering and longitudinal acceleration controllers. This framework is validated within a simulation study and has been deployed on our autonomous vehicle shown in Fig.1 that has travelled thousands of miles in urban environments.