TDR-OBCA: A Reliable Planner for Autonomous Driving in Free-Space Environment

by   Runxin He, et al.

This paper presents an optimization-based collision avoidance trajectory generation method for autonomous driving in free-space environments, with enhanced robust-ness, driving comfort and efficiency. Starting from the hybrid optimization-based framework, we introduces two warm start methods, temporal and dual variable warm starts, to improve the efficiency. We also reformulates the problem to improve the robustness and efficiency. We name this new algorithm TDR-OBCA. With these changes, compared with original hybrid optimization we achieve a 96.67 conditions, 13.53 planner efficiency as obstacles number scales. We validate our results in hundreds of simulation scenarios and hundreds of hours of public road tests in both U.S. and China. Our source code is availableat


page 5

page 6

page 7


DL-IAPS and PJSO: A Path/Speed Decoupled Trajectory Optimization and its Application in Autonomous Driving

This paper presents a free space trajectory optimization algorithm of au...

NMPC trajectory planner for urban autonomous driving

This paper presents a trajectory planner for autonomous driving based on...

An Auto-tuning Framework for Autonomous Vehicles

Many autonomous driving motion planners generate trajectories by optimiz...

Spatial Constraint Generation for Motion Planning in Dynamic Environments

This paper presents a novel method to generate spatial constraints for m...

SVM Enhanced Frenet Frame Planner For Safe Navigation Amidst Moving Agents

This paper proposes an SVM Enhanced Trajectory Planner for dynamic scene...

Time-Dependent Hybrid-State A* and Optimal Control for Autonomous Vehicles in Arbitrary and Dynamic Environment

The development of driving functions for autonomous vehicles in urban en...

Trajectory Optimization for Curvature Bounded Non-Holonomic Vehicles: Application to Autonomous Driving

In this paper, we propose a trajectory optimization for computing smooth...