AI 中文总结
本研究提出定时规则监督的PSS，干预端到端泊车策略的故障模式，在CARLA模拟中成功率从85.16%提升至97.66%。
AI 中文摘要
我们研究了一个手动指定的运行时监督器能否在固定的CARLA停车场中纠正现有端到端泊车策略的重复性故障。基于视觉的Transformer架构继承自Yang等人；我们的贡献是对其控制输出应用一个定时、基于规则的参数化安全防护盾（PSS）。PSS使用手动校准的速度、位置和持续时间阈值来干预观察到的故障模式，包括边界退出、延迟制动以及停滞或振荡控制。在报告的闭环评估中，16个保留的目标车位和六个初始姿态各在四轮中评估（每次配置384次尝试）。目标成功率从重训练策略的327/384（85.16%）提高到使用PSS的375/384（97.66%）；成功尝试中的平均位置和方向误差分别为0.21米和0.33度。这些结果表明在此模拟器设置内有所改进。重复尝试共享一个地图、车辆和传感器配置，且PSS使用模拟器世界坐标；因此，结果不能证明对其他停车场或真实车辆的泛化性，也不提供正式的安全保证。
英文摘要
We study whether a manually specified runtime supervisor can correct recurring failures of an existing end-to-end parking policy in a fixed CARLA parking lot. The vision-based Transformer architecture is inherited from Yang et al.; our contribution is a timed, rule-based Parametric Safety Shield (PSS) applied to its control outputs. The PSS uses hand-calibrated speed, position, and duration thresholds to intervene in observed failure modes, including boundary exits, delayed braking, and stalled or oscillatory control. In the reported closed-loop evaluation, 16 held-out target slots and six initial poses are each evaluated in four rounds (384 attempts per configuration). Target success increases from 327/384 (85.16%) for the retrained policy to 375/384 (97.66%) with the PSS; mean position and orientation errors among successful attempts are 0.21m and 0.33 degrees. These results show an improvement within this simulator setup. The repeated attempts share one map, vehicle, and sensor configuration, and the PSS uses simulator world coordinates; thus the results do not establish generalization to other lots or real vehicles, or a formal safety guarantee.
