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

自动驾驶汽车依靠学习功能的组件进行关键操作，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情况的罕见性，它是耗时的，有风险的，而且是不够的。高保真模拟提供了一种很有前途的方式来补充这些努力，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。这就提出了一些关键问题：我们如何在模拟中生成罕见但真实的交通情况，以真正测试自动驾驶汽车的安全性？此外，我们如何才能不断改进自动驾驶汽车的软件，从每一个发现的故障中学习？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罕见但现实的场景，这些场景可能会导致自动驾驶汽车发生故障，然后增强软件以确保这些故障不会再次发生。该研究的意义不仅限于安全改进，还可能重新定义行业实践，塑造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。该项目将开发一个新的框架，名为CRASH -基于强化学习的安全强化对抗场景。CRASH利用一种新型的多智能体对抗性深度强化学习设置，自动有效地对现有的自动驾驶汽车软件栈进行压力测试，帮助识别运动规划中的潜在故障。然后，它通过提高其避免重复这些故障并从中学习的能力来提高AV的安全性能。值得注意的是，CRASH强调了识别出的AV故障的现实、合理和自然的方面，反映了现实世界交通条件中的意外情况。CRASH的真实的优势在于其迭代过程，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-该团队称之为安全强化。这种迭代改进增强了AV的安全性，使其能够更有效地导航意外的交通情况，从而提高其弹性。该项目提供了一个务实和可靠的途径，以推进严重依赖学习的自动驾驶汽车的安全测试-使组件，使他们可以导航我们的道路与增强的安全性和鲁棒性水平。这项研究是由国家科学基金会和开放慈善机构之间的合作伙伴关系的支持。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用的支持，基金会的学术价值和更广泛的影响审查标准。

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.