Autonomous driving systems (ADS) have achieved remarkable progress in recent years. However, ensuring their safety and reliability remains a critical challenge due to the complexity and uncertainty of driving scenarios. In this paper, we focus on simulation testing for ADS, where generating diverse and effective testing scenarios is a central task. Existing fuzz testing methods face limitations, such as overlooking the temporal and spatial dynamics of scenarios and failing to leverage simulation feedback (e.g., speed, acceleration and heading) to guide scenario selection and mutation. To address these issues, we propose SimADFuzz, a novel framework designed to generate high-quality scenarios that reveal violations in ADS behavior. Specifically, SimADFuzz employs violation prediction models, which evaluate the likelihood of ADS violations, to optimize scenario selection. Moreover, SimADFuzz proposes distance-guided mutation strategies to enhance interactions among vehicles in offspring scenarios, thereby triggering more edge-case behaviors of vehicles. Comprehensive experiments demonstrate that SimADFuzz outperforms state-of-the-art fuzzers by identifying 32 more unique violations, including 4 reproducible cases of vehicle-vehicle and vehicle-pedestrian collisions. These results demonstrate SimADFuzz's effectiveness in enhancing the robustness and safety of autonomous driving systems.

翻译：自动驾驶系统近年来取得了显著进展。然而，由于驾驶场景的复杂性和不确定性，确保其安全性和可靠性仍是一项关键挑战。本文聚焦于自动驾驶系统的仿真测试，其中生成多样且有效的测试场景是核心任务。现有模糊测试方法存在局限性，例如忽视场景的时空动态特性，且未能利用仿真反馈（如速度、加速度和航向）来指导场景选择与变异。为解决这些问题，我们提出SimADFuzz——一种旨在生成高质量场景以揭示自动驾驶系统行为违规的新型框架。具体而言，SimADFuzz采用违规预测模型（用于评估自动驾驶系统违规可能性）来优化场景选择。此外，SimADFuzz提出距离引导的变异策略，以增强衍生场景中车辆间的交互，从而触发更多边缘情况的车辆行为。综合实验表明，SimADFuzz相较于最先进的模糊测试工具，能多识别出32个独立违规案例，其中包括4个可复现的车辆-车辆及车辆-行人碰撞案例。这些结果证明了SimADFuzz在提升自动驾驶系统鲁棒性与安全性方面的有效性。