Connected and automated vehicles (CAVs) have the potential to enhance driving safety, for example by enabling safe vehicle following and more efficient traffic scheduling. For such future deployments, safety requirements should be addressed, where the primary such are avoidance of vehicle collisions and substantial mitigating of harm when collisions are unavoidable. However, conservative worst-case-based control strategies come at the price of reduced flexibility and may compromise overall performance. In light of this, we investigate how Deep Reinforcement Learning (DRL) can be leveraged to improve safety in multi-vehicle-following scenarios involving emergency braking. Specifically, we investigate how DRL with vehicle-to-vehicle communication can be used to ethically select an emergency breaking profile in scenarios where overall, or collective, three-vehicle harm reduction or collision avoidance shall be obtained instead of single-vehicle such. As an algorithm, we provide a hybrid approach that combines DRL with a previously published method based on analytical expressions for selecting optimal constant deceleration. By combining DRL with the previous method, the proposed hybrid approach increases the reliability compared to standalone DRL, while achieving superior performance in terms of overall harm reduction and collision avoidance.

翻译：网联自动驾驶车辆（CAVs）具有提升驾驶安全性的潜力，例如通过实现安全跟车与更高效的交通调度。针对此类未来部署场景，需满足安全性要求，其核心在于避免车辆碰撞，并在碰撞不可避免时显著减轻损害。然而，基于保守最坏情况的控制策略会牺牲灵活性，并可能影响整体性能。鉴于此，本研究探讨如何利用深度强化学习（DRL）在多车跟驰场景中提升紧急制动安全性。具体而言，我们研究了在需实现三车整体（或集体）伤害降低或碰撞避免（而非单车目标）的场景中，如何通过结合车对车通信的DRL技术伦理选择紧急制动曲线。在算法层面，我们提出一种混合方法，将DRL与先前发表的基于解析表达式选择最优恒定减速度的方法相结合。通过融合DRL与既有方法，所提出的混合方案在提升整体伤害降低与碰撞避免性能的同时，较独立DRL方案具有更高的可靠性。