SLES: CRASH - Challenging Reinforcement-learning based Adversarial scenarios for Safety Hardening

自动驾驶汽车的关键操作依赖于具有学习功能的组件，这为交通运输带来了令人兴奋的未来。然而，在充满“未知的未知”的不可预测的现实交通场景中，确保这些车辆的安全仍然是一个重大障碍。虽然道路测试是必不可少的，但由于安全关键交通状况的罕见，它既耗时又有风险，而且不够充分。高保真模拟为这些努力提供了一种有希望的补充方式，使我们能够在无数具有挑战性的场景中对自动驾驶汽车进行压力测试。这就提出了一个关键问题：我们如何在模拟中生成罕见但真实的交通状况，从而真正对自动驾驶汽车的安全性进行压力测试？此外，我们如何不断改进自动驾驶汽车的软件，从每一个已识别的故障中学习？作为回应，该项目提供了一种创新的方法，我们有目的地在模拟中引入可能导致自动驾驶汽车故障的罕见但现实的场景，然后增强软件以确保这些故障不会再次发生。这项研究的意义超出了安全改进，它有可能重新定义行业实践，形成自动驾驶汽车安全的监管框架，并确保自动驾驶汽车的安全可靠部署。该项目将开发一个名为CRASH的新框架——基于挑战强化学习的安全加固对抗场景。CRASH利用一种新型的多智能体对抗深度强化学习设置，自动有效地对现有的自动驾驶汽车软件堆栈进行压力测试，帮助识别运动规划中的潜在故障。然后，通过提高自动驾驶汽车避免重复这些故障并从中吸取教训的能力，提高自动驾驶汽车的安全性能。值得注意的是，CRASH强调已识别的自动驾驶汽车故障的现实、合理和自然方面，反映了现实交通条件下的意外情况。CRASH的真正优势在于它的迭代过程，在每次伪造之后，改进模拟会导致自动驾驶车辆堆栈的持续增强，该团队将这种方法称为安全强化。这种迭代改进强化了自动驾驶汽车的安全性，使其能够更有效地应对意外的交通状况，从而提高其弹性。该项目为推进严重依赖学习组件的自动驾驶汽车的安全测试提供了一条实用可靠的途径，从而使它们能够以更高的安全性和稳健性在道路上行驶。这项研究得到了美国国家科学基金会和开放慈善机构的合作支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Autonomous vehicles, with their reliance on learning-enabled components for key operations, promise an exciting future for transportation. Yet, assuring the safety of these vehicles amid unpredictable real-world traffic scenarios filled with 'unknown unknowns' remains a significant hurdle. While on-road testing is essential, it is time-consuming, risky, and insufficient due to the rarity of safety-critical traffic situations. High-fidelity simulations present a promising way to complement these efforts, allowing us to stress-test autonomous vehicles in a myriad of challenging scenarios. This raises key questions: how can we generate rare, but realistic traffic situations in simulation that would truly stress test an autonomous vehicle's safety? Moreover, how can we continuously improve the autonomous vehicle's software to learn from each identified failure? In response, this project offers an innovative approach where we purposefully introduce rare but realistic scenarios in simulation that may cause autonomous vehicles to fail, and then enhance the software to ensure these failures do not reoccur. The implications of the research extends beyond safety improvements, having the potential to redefine industry practices, shape regulatory frameworks for autonomous vehicle safety, and ensure the safe and reliable deployment of autonomous vehicles.The project will develop a new framework, named CRASH - Challenging Reinforcement-learning based Adversarial scenarios for Safety Hardening. CRASH leverages a novel multi-agent adversarial deep reinforcement learning setting to automatically and effectively stress test existing autonomous vehicle software stacks, helping identify potential failures in motion planning. It then enhances the AV's safety performance by improving its ability to avoid repeating these failures and learn from them. Notably, CRASH emphasizes the realistic, plausible, and naturalistic aspects of identified AV failures, mirroring unexpected situations in real-world traffic conditions. The real strength of CRASH is its iterative process, where after each falsification, an improvement simulation leads to continuous enhancement of the autonomous vehicle stack - an approach the team termed safety hardening. This iterative refinement fortifies an AV's safety, allowing it to navigate unexpected traffic situations more efficiently, thereby increasing its resilience. The project provides a pragmatic and reliable pathway to advance the safety testing of autonomous vehicles that rely heavily on learning-enabled components so that they can navigate our roads with an enhanced level of safety and robustness.This research is supported by a partnership between the National Science Foundation and Open Philanthropy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.