Sampling-Based Motion Planning for Autonomous Driving on 3D Race Tracks

Khái niệm cốt lõi

Sampling-based local trajectory planning for autonomous racing on complex 3D race tracks improves lap times and dynamic utilization.

Tóm tắt

The content discusses a sampling-based motion planning approach for autonomous racing on three-dimensional race tracks. It introduces the challenges faced by existing methods on complex circuits and the limitations of two-dimensional tracks. The proposed approach aims to maintain lap times on complex tracks, consider 3D effects, and reduce lap times in multi-vehicle scenarios. The content is structured into sections covering methodology, trajectory generation, feasibility checks, trajectory selection, and results and discussion. Experiments are conducted on the Las Vegas Motor Speedway and the Mount Panorama Circuit in Bathurst to evaluate the effectiveness of the proposed approach.

Thống kê

"In simulative experiments, we demonstrate that our approach achieves lower lap times and improved utilization of dynamic limits compared to existing approaches."
"The 3D effects are considered by constraining the combined lateral and longitudinal acceleration by diamond-shaped gg-diagrams that depend on both velocity and vertical acceleration."

Trích dẫn

"We show that the jerk-optimal trajectory generation used in existing sampling-based planning approaches is sufficient for oval race tracks but unsuitable for complex ones."
"The main contributions can be summarized as follows: We show that the jerk-optimal trajectory generation used in existing sampling-based planning approaches is sufficient for oval race tracks but unsuitable for complex ones."

Thông tin chi tiết chính được chắt lọc từ

Sampling-Based Motion Planning with Online Racing Line Generation for Autonomous Driving on Three-Dimensional Race Tracks

by Leve... lúc arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18643.pdf

Yêu cầu sâu hơn

How can the proposed approach be adapted for real-world applications in autonomous racing?

The proposed approach of sampling-based local trajectory planning with online racing line generation can be adapted for real-world applications in autonomous racing by integrating it into the software stack of autonomous racing vehicles. This would involve implementing the trajectory planning algorithm on the actual autonomous vehicle hardware and software systems. The algorithm would need to communicate with the vehicle's sensors to receive real-time data about the environment and other vehicles. Additionally, the algorithm would need to interface with the vehicle's control system to execute the planned trajectories effectively. Extensive testing and validation in simulation and real-world scenarios would be crucial to ensure the safety and performance of the system. Furthermore, the algorithm could be optimized for real-time performance to handle the dynamic and unpredictable nature of racing scenarios.

What are the potential drawbacks of relying on online racing line generation for trajectory planning?

While online racing line generation offers the advantage of adapting to the current vehicle state and potentially reducing lap times, there are some potential drawbacks to consider. One drawback is the computational complexity of generating the racing line online, especially within a limited spatial horizon. This could lead to increased processing time and potentially impact the real-time responsiveness of the system. Additionally, the accuracy of the online racing line generation may be affected by uncertainties in the environment, such as the presence of other vehicles or dynamic track conditions. This could result in suboptimal trajectories being generated, impacting the overall performance of the autonomous racing system. Moreover, the reliance on online racing line generation may introduce additional complexity and potential points of failure in the system, requiring robust error handling and validation mechanisms.

How can the concept of relative trajectory generation be extended to handle more complex maneuvers beyond overtaking scenarios?

To extend the concept of relative trajectory generation to handle more complex maneuvers beyond overtaking scenarios, the algorithm can be enhanced to incorporate a wider range of maneuvering options and constraints. One approach could involve integrating different motion primitives or trajectory generation techniques tailored to specific maneuver types, such as aggressive cornering, acceleration bursts, or obstacle avoidance. By expanding the set of available trajectories and optimizing them based on the relative racing line, the system can adapt to various challenging scenarios. Additionally, the concept of relative trajectory generation can be extended to include predictive modeling of the environment, enabling the system to anticipate and plan for complex maneuvers proactively. This could involve incorporating machine learning algorithms to learn from past experiences and improve decision-making in real-time. Overall, by enhancing the flexibility and adaptability of the relative trajectory generation approach, the system can effectively handle a wide range of complex racing scenarios beyond simple overtaking maneuvers.