Generating safety-critical scenarios, which are essential yet difficult to
collect at scale, offers an effective method to evaluate the robustness of
autonomous vehicles (AVs). Existing methods focus on optimizing adversariality
while preserving the naturalness of scenarios, aiming to achieve a balance
through data-driven approaches. However, without an appropriate upper bound for
adversariality, the scenarios might exhibit excessive adversariality,
potentially leading to unavoidable collisions. In this paper, we introduce
FREA, a novel safety-critical scenarios generation method that incorporates the
Largest Feasible Region (LFR) of AV as guidance to ensure the reasonableness of
the adversarial scenarios. Concretely, FREA initially pre-calculates the LFR of
AV from offline datasets. Subsequently, it learns a reasonable adversarial
policy that controls critical background vehicles (CBVs) in the scene to
generate adversarial yet AV-feasible scenarios by maximizing a novel
feasibility-dependent objective function. Extensive experiments illustrate that
FREA can effectively generate safety-critical scenarios, yielding considerable
near-miss events while ensuring AV's feasibility. Generalization analysis also
confirms the robustness of FREA in AV testing across various surrogate AV
methods and traffic environments.

FREA 是一种新的安全关键场景生成方法，它通过将 AV 的最大可行域作为引导，确保对抗性场景的合理性。FREA 能够有效地生成安全关键场景，并在保证 AV 的可行性的同时产生了相当多的近撞事件。广泛的实验也确认了 FREA 在各种代理 AV 方法和交通环境中进行 AV 测试的鲁棒性。

FREA: 可行性指导下合理对抗性生成安全关键场景

FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with  Reasonable Adversariality

Autonomous driving and its widespread adoption have long held tremendous
promise. Nevertheless, without a trustworthy and thorough testing procedure,
not only does the industry struggle to mass-produce autonomous vehicles (AV),
but neither the general public nor policymakers are convinced to accept the
innovations. Generating safety-critical scenarios that present significant
challenges to AV is an essential first step in testing. Real-world datasets
include naturalistic but overly safe driving behaviors, whereas simulation
would allow for unrestricted exploration of diverse and aggressive traffic
scenarios. Conversely, higher-dimensional searching space in simulation
disables efficient scenario generation without real-world data distribution as
implicit constraints. In order to marry the benefits of both, it seems
appealing to learn to generate scenarios from both offline real-world and
online simulation data simultaneously. Therefore, we tailor a Reversely
Regularized Hybrid Offline-and-Online ((Re)$^2$H2O) Reinforcement Learning
recipe to additionally penalize Q-values on real-world data and reward Q-values
on simulated data, which ensures the generated scenarios are both varied and
adversarial. Through extensive experiments, our solution proves to produce more
risky scenarios than competitive baselines and it can generalize to work with
various autonomous driving models. In addition, these generated scenarios are
also corroborated to be capable of fine-tuning AV performance.

利用反向正则化混合离线 - 在线强化学习模型同时学习真实世界和模拟数据来生成自动驾驶测试场景，实现更高效的 AV 测试和更准确的风险评估。