让自动驾驶在延迟中安全决策，通过动态地图预警碰撞风险。
LAVQA: A Latency-Aware Visual Question Answering Framework for Shared Autonomy in Self-Driving Vehicles
- 用动态碰撞图融合延迟与障碍物不确定性，实时反映安全区变化
- 在CARLA仿真中，碰撞率降低超过8倍，显著优于无延迟感知方法
- 适合需要远程协作的自动驾驶系统，尤其应对网络延迟场景
当不确定性较高时，自动驾驶车辆可能因安全考虑而停驶，并需借助远程人类操作员提供高层指导。这种模式称为「共享自主」，使自动驾驶系统与远程操作员协同制定响应策略。为应对由无线网络延迟和人工响应时间导致的可变延迟所引发的关键决策时机问题，本文提出LAVQA——一种延迟感知的共享自主框架，整合了视觉问答（VQA）与时空风险可视化技术。LAVQA通过引入动态演化的延迟诱发碰撞图（LICOM），将时间延迟与空间不确定性共同表征，使远程操作员能够实时观察车辆安全区域随时间的变化，尤其是在存在动态障碍物和响应延迟的情况下。在标准自动驾驶仿真平台CARLA中的闭环测试表明，相较于无延迟感知的基线方法，LAVQA可使碰撞率降低超过8倍。
原文摘要 · Abstract (English)
When uncertainty is high, self-driving vehicles may halt for safety and benefit from the access to remote human operators who can provide high-level guidance. This paradigm, known as {shared autonomy}, enables autonomous vehicle and remote human operators to jointly formulate appropriate responses. To address critical decision timing with variable latency due to wireless network delays and human response time, we present LAVQA, a latency-aware shared autonomy framework that integrates Visual Question Answering (VQA) and spatiotemporal risk visualization. LAVQA augments visual queries with Latency-Induced COllision Map (LICOM), a dynamically evolving map that represents both temporal latency and spatial uncertainty. It enables remote operator to observe as the vehicle safety regions vary over time in the presence of dynamic obstacles and delayed responses. Closed-loop simulations in CARLA, the de-facto standard for autonomous vehicle simulator, suggest that that LAVQA can reduce collision rates by over 8x compared to latency-agnostic baselines.
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