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SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees (2407.16857v1)

Published 23 Jul 2024 in cs.RO, cs.LG, and stat.ML

Abstract: Over the last decade, there has been increasing interest in autonomous driving systems. Reinforcement Learning (RL) shows great promise for training autonomous driving controllers, being able to directly optimize a combination of criteria such as efficiency comfort, and stability. However, RL- based controllers typically offer no safety guarantees, making their readiness for real deployment questionable. In this paper, we propose SECRM-2D (the Safe, Efficient and Comfortable RL- based driving Model with Lane-Changing), an RL autonomous driving controller (both longitudinal and lateral) that balances optimization of efficiency and comfort and follows a fixed route, while being subject to hard analytic safety constraints. The aforementioned safety constraints are derived from the criterion that the follower vehicle must have sufficient headway to be able to avoid a crash if the leader vehicle brakes suddenly. We evaluate SECRM-2D against several learning and non-learning baselines in simulated test scenarios, including freeway driving, exiting, merging, and emergency braking. Our results confirm that representative previously-published RL AV controllers may crash in both training and testing, even if they are optimizing a safety objective. By contrast, our controller SECRM-2D is successful in avoiding crashes during both training and testing, improves over the baselines in measures of efficiency and comfort, and is more faithful in following the prescribed route. In addition, we achieve a good theoretical understanding of the longitudinal steady-state of a collection of SECRM-2D vehicles.

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Summary

  • The paper introduces an RL-based framework that integrates hard analytic safety constraints for both lateral and longitudinal vehicle control.
  • It unifies discretionary and mandatory lane changes into a single optimization objective to enhance efficiency and comfort.
  • Simulation results in SUMO demonstrate robust performance with a 0% crash rate and improved route adherence under varied traffic conditions.

SECRM-2D: RL-Based Efficient and Comfortable Autonomous Driving with Analytical Safety Guarantees

The task of developing autonomous vehicles (AVs) that can drive safely and efficiently in dynamic environments poses significant challenges. Among various techniques, Reinforcement Learning (RL) has shown considerable potential for optimizing multiple performance metrics of AVs, such as efficiency, comfort, and stability. However, the lack of explicit safety guarantees often hinders the deployment of RL-based controllers in real-world scenarios. This paper presents SECRM-2D, a robust RL-based model that aims to bridge this gap by integrating hard analytic safety constraints applicable to both longitudinal and lateral vehicle control.

Core Contributions

SECRM-2D introduces a decision-making framework for autonomous vehicles that simultaneously optimizes efficiency, comfort, and route adherence, all while ensuring compliance with strict safety protocols. Key innovations include:

  • Analytic Safety Constraints: The paper extends previous longitudinal safety constraints to lateral (lane-changing) scenarios, establishing a model that supports both discretionary and mandatory lane changes. Safety constraints are derived from principles akin to the Vienna convention on road traffic, providing explicit certifiable safety guarantees.
  • Unified Lane Change Framework: The model uniquely handles discretionary and mandatory lane changes within a single optimization objective, enhancing its applicability in diverse traffic scenarios.
  • Steady-State Platooning Analysis: The authors present a theoretical analysis of a platoon of SECRM-2D controlled vehicles reaching a steady-state, thus offering insights into long-term behavior under varying traffic conditions. The researchers derive explicit formulas linking the gap between vehicles to platoon speed, implicating broader traffic flow implications.

Experimental Evaluation and Insights

The evaluation comprises simulated environments modeled in the SUMO microscopic traffic simulator. The operational scope tested includes both a simple loop network and a more complex freeway structure based on real-world geometries.

  • Discretionary Lane-Change Scenarios: In a basic loop network, SECRM-2D demonstrated notable improvements in both efficiency and comfort over baseline models like IDM+MOBIL, Gipps with Greedy Lane Selection, and a PPO-based lane-change algorithm. SECRM-2D consistently maintained a 0% crash rate, illustrating its robustness in diverse traffic conditions, including emergency braking.
  • Route-Following Scenarios: SECRM-2D navigated a freeway interchange with high efficacy, showcasing the framework's capability to generalize route adherence across varied network conditions. The controller leveraged its RL architecture to learn effective bypassing tactics in congested traffic, thus outperforming several baselines in both speed stability and safety adherence.

Implications for Autonomous Driving

The paper's results reinforce the viability of combining RL with explicit safety guarantees in training AV controllers. By addressing both the efficiency-comfort trade-off and the critical issue of safety, SECRM-2D contributes to unlocking possibilities for safer real-world deployment of autonomous vehicles.

Future Directions

The broader implications of SECRM-2D suggest several intriguing avenues for future research. While the current model effectively utilizes high-level analytic safety constraints, further work could explore more granular safety improvements, potentially addressing cases with uncertain sensor readings or variable reaction times among mixed traffic participants. Additionally, extending theoretical analyses to cover more complex interaction scenarios, such as multi-agent systems or mixed human and autonomous traffic environments, would provide even more comprehensive insights.

Combining RL frameworks with explicit safety constraints offers a promising path toward the practical realization of autonomous driving. SECRM-2D exemplifies how rigorous safety protocols can be harmonized with advanced RL strategies to create robust, deployable AV systems.

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