论文标题
使用安全部队在CARLA模拟器上实施的安全部队的自动驾驶政策的理由意识到的自动驾驶政策
Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulator
论文作者
论文摘要
尽管近年来自动驾驶技术迅速改善，但汽车制造商必须解决责任问题，以使SAE J3016 3或更高级别的自动驾驶汽车商业化。为了应对产品责任法，制造商根据国际安全标准（例如ISO 26262和ISO 21448）开发自动驾驶系统。关于ISO 26262中预期功能（SOTIF）要求的安全性，在ISO 26262中，驾驶策略建议提供机动决策的明确基础。在这种情况下，可能是合适的数学模型，例如安全部队（SFF）和对决策的责任敏感安全性（RSS）。在这项工作中，我们从头开始实现SFF，以替换未公开的NVIDIA的源代码，并将其与Carla开源模拟器集成在一起。使用SFF和Carla，我们为声称的一组车辆提供了预测指标，并基于预测指标，提出了一项集成的驾驶策略，无论其在通过动态流量时遇到的安全条件如何，都可以始终如一地运行。该政策没有针对每种条件的单独计划，但是使用安全潜力，它旨在与人类驾驶与交通流量融为一体。
Despite the rapid improvement of autonomous driving technology in recent years, automotive manufacturers must resolve liability issues to commercialize autonomous passenger car of SAE J3016 Level 3 or higher. To cope with the product liability law, manufacturers develop autonomous driving systems in compliance with international standards for safety such as ISO 26262 and ISO 21448. Concerning the safety of the intended functionality (SOTIF) requirement in ISO 26262, the driving policy recommends providing an explicit rational basis for maneuver decisions. In this case, mathematical models such as Safety Force Field (SFF) and Responsibility-Sensitive Safety (RSS) which have interpretability on decision, may be suitable. In this work, we implement SFF from scratch to substitute the undisclosed NVIDIA's source code and integrate it with CARLA open-source simulator. Using SFF and CARLA, we present a predictor for claimed sets of vehicles, and based on the predictor, propose an integrated driving policy that consistently operates regardless of safety conditions it encounters while passing through dynamic traffic. The policy does not have a separate plan for each condition, but using safety potential, it aims human-like driving blended in with traffic flow.
