The growing advancements in Autonomous Vehicles (AVs) have emphasized the
critical need to prioritize the absolute safety of AV maneuvers, especially in
dynamic and unpredictable environments or situations. This objective becomes
even more challenging due to the uniqueness of every traffic
situation/condition. To cope with all these very constrained and complex
configurations, AVs must have appropriate control architectures with reliable
and real-time Risk Assessment and Management Strategies (RAMS). These targeted
RAMS must lead to reduce drastically the navigation risks. However, the lack of
safety guarantees proves, which is one of the key challenges to be addressed,
limit drastically the ambition to introduce more broadly AVs on our roads and
restrict the use of AVs to very limited use cases. Therefore, the focus and the
ambition of this paper is to survey research on autonomous vehicles while
focusing on the important topic of safety guarantee of AVs. For this purpose,
it is proposed to review research on relevant methods and concepts defining an
overall control architecture for AVs, with an emphasis on the safety assessment
and decision-making systems composing these architectures. Moreover, it is
intended through this reviewing process to highlight researches that use either
model-based methods or AI-based approaches. This is performed while emphasizing
the strengths and weaknesses of each methodology and investigating the research
that proposes a comprehensive multi-modal design that combines model-based and
AI approaches. This paper ends with discussions on the methods used to
guarantee the safety of AVs namely: safety verification techniques and the
standardization/generalization of safety frameworks.

本文调查研究自动驾驶汽车的安全性保障问题，重点关注控制架构、风险评估与管理策略、基于人工智能和基于模型的方法的研究，并探讨了安全保障技术和安全框架的标准化 / 概括方法。

自动驾驶车辆安全性：基于模型与基于人工智能两种方法的调查

Safety of autonomous vehicles: A survey on Model-based vs. AI-based  approaches

Despite the tremendous success of Reinforcement Learning (RL) algorithms in
simulation environments, applying RL to real-world applications still faces
many challenges. A major concern is safety, in another word, constraint
satisfaction. State-wise constraints are one of the most common constraints in
real-world applications and one of the most challenging constraints in Safe RL.
Enforcing state-wise constraints is necessary and essential to many challenging
tasks such as autonomous driving, robot manipulation. This paper provides a
comprehensive review of existing approaches that address state-wise constraints
in RL. Under the framework of State-wise Constrained Markov Decision Process
(SCMDP), we will discuss the connections, differences, and trade-offs of
existing approaches in terms of (i) safety guarantee and scalability, (ii)
safety and reward performance, and (iii) safety after convergence and during
training. We also summarize limitations of current methods and discuss
potential future directions.

本文综述了在强化学习中解决状态限制问题的现存方法并比较了它们在安全性、可伸缩性、奖励表现等方面的差异和权衡，同时总结了当前方法的局限性并探讨了未来的研究方向。

基于状态的安全强化学习：一项调查

State-wise Safe Reinforcement Learning: A Survey

We propose a safe exploration algorithm for deterministic Markov Decision
Processes with unknown transition models. Our algorithm guarantees safety by
leveraging Lipschitz-continuity to ensure that no unsafe states are visited
during exploration. Unlike many other existing techniques, the provided safety
guarantee is deterministic. Our algorithm is optimized to reduce the number of
actions needed for exploring the safe space. We demonstrate the performance of
our algorithm in comparison with baseline methods in simulation on navigation
tasks.

本文提出一种基于 Lipschitz 连续性的确定性马尔可夫决策过程未知转移模型的安全探索算法，该算法通过优化减少探索安全空间所需的操作数量，并在导航任务的仿真中与基线方法进行了性能比较。