LAVQA: 自動運転車の共有自律のための遅延認識型視覚質問応答フレームワーク
LAVQA: A Latency-Aware Visual Question Answering Framework for Shared Autonomy in Self-Driving Vehicles
無線遅延と人間の応答時間を考慮した衝突リスクマップを視覚質問応答に組み込み、遠隔操作者が安全に自動運転車を支援できる枠組みを提案し、CARLAシミュレーションで衝突率を8倍以上削減した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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5. 議論はある？
6. 次に読むべき論文は？
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著者: Shuangyu Xie, Kaiyuan Chen, Wenjing Chen, Chengyuan Qian, Christian Juette, Liu Ren, Dezhen Song, Ken Goldberg
分類: cs.RO
原文アブストラクト
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.
