An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments (2208.13160v2)

Abstract: As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community

• The paper introduces a spatial-temporal joint optimization approach that leverages differential flatness for efficient vehicle trajectory planning.
• It employs piece-wise polynomial trajectory representation and safe driving corridors to achieve effective real-time obstacle avoidance.
• Benchmark results demonstrate an order-of-magnitude improvement in planning time and superior trajectory quality for safer autonomous driving.

An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments: A Summary

The paper "An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments" addresses the challenge of real-time trajectory planning for autonomous vehicles. It specifically focuses on environments characterized by nonholonomic dynamics and dynamic obstacles. The authors propose an advanced trajectory optimization method leveraging differential flatness to simplify trajectory representation while maintaining compliance with nonholonomic dynamics.

Core Contributions and Methodology

The authors introduce a spatial-temporal joint optimization approach that constructs a trajectory optimally in terms of both space and time. This method surpasses traditional decoupled approaches, utilizing the vehicle's differential flatness property to streamline trajectory planning. Key features of the methodology include:

• Trajectory Parameterization: The trajectory is represented in a flat-output space as piece-wise polynomials, allowing for a simplified representation and efficient optimization.
• Obstacle Avoidance: The planner achieves efficiency in obstacle avoidance by using a safe driving corridor and signed distance approximations to better handle dynamic obstacles.
• Differential Flatness Utilization: By using flat outputs, the paper effectively encodes vehicle dynamics constraints and spatial-temporal relationships, enabling more flexible trajectory optimization.

Numerical Results and Comparisons

The paper provides benchmarks against state-of-the-art methods, demonstrating superior performance in terms of both efficiency and trajectory quality. The approach optimizes control efforts, effectively balancing trajectory quality and computation time across various complex scenarios. In particular:

• Time Efficiency: The proposed method shows an order-of-magnitude improvement in planning time over competing methods, especially in large-scale environments with numerous obstacles.
• Trajectory Quality: It offers significant enhancements in trajectory quality metrics—such as mean acceleration and jerk—indicating better safety and passenger comfort.

Practical and Theoretical Implications

Practically, this research has significant implications for real-world autonomous vehicle applications, enabling safer and more efficient navigation in complex environments. Theoretically, it contributes to the domain of optimal control and robotics by demonstrating advanced usage of differential flatness and continuous polynomial trajectory representations.

Future Directions

Future work could explore extending this trajectory planning framework to multi-robot systems or adapting the principles to other types of nonholonomic robots. Further enhancements may include incorporating real-time feedback and adaptability to continuously evolving environments.

In conclusion, this paper provides a comprehensive solution to some of the fundamental challenges in motion planning for autonomous vehicles, promising advancements in both theoretical understanding and practical applications within the field.