Spatio-Temporal Joint Trajectory Planning for Autonomous Vehicles Based on Improved Constrained Iterative LQR
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View abstract on PubMed
Summary
This summary is machine-generated.This study enhances autonomous driving trajectory planning by improving the Constrained Iterative Linear Quadratic Regulator (CILQR) for better human-like driving and traffic efficiency. The new method significantly boosts performance and reduces computation time.
Area Of Science
- Robotics
- Control Systems
- Artificial Intelligence
Background
- Autonomous driving requires sophisticated spatio-temporal joint trajectory planning.
- Traditional methods struggle with complex scenarios, highlighting limitations of sequential decoupling.
- Constrained Iterative Linear Quadratic Regulator (CILQR) shows promise but needs efficiency and adaptability improvements.
Purpose Of The Study
- To enhance the Constrained Iterative Linear Quadratic Regulator (CILQR) for autonomous driving trajectory planning.
- To improve computational efficiency, scenario adaptability, and driving comfort.
- To achieve more human-like driving and increased traffic efficiency.
Main Methods
- Implemented a segmented barrier function truncation strategy with dynamic relaxation factors for stability.
- Introduced an adaptive weight parameter adjustment for acceleration and curvature planning.
- Integrated the hybrid A* algorithm to optimize initial trajectories and enhance iterative efficiency.
Main Results
- Demonstrated substantial improvements in human-like driving performance (16.35% increase).
- Achieved a 12.65% average increase in traffic efficiency.
- Reduced computation time by 39.29% while maintaining driving comfort.
Conclusions
- The improved CILQR method offers significant advancements in autonomous driving trajectory planning.
- The enhancements lead to more efficient, adaptable, and human-like autonomous navigation.
- Validated through simulations and real-vehicle tests, proving practical applicability.
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