  This paper presents an optimization-based collision avoidance trajectory
generation method for autonomous driving in free-space environments, with
enhanced robustness, 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 reformulate
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% failure rate decrease with respect to initial
conditions, 13.53% increase in driving comforts and 3.33% to 44.82% increase in
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 available at
https://github.com/ApolloAuto/apollo.
