自動運転は雪道に耐えられるか:悪天候下の堅牢な自動運転に向けた課題
Are We Ready for Snow-tonomous Driving? Challenges Toward Robust Automated Driving in Extreme Weather
EUプロジェクトROADVIEWの4年間の開発と実証から、雪・雨・霧などの悪天候下での自動運転を妨げる10の課題(データ不足、ドメインシフト、マルチモーダル知覚の限界、制御の堅牢性、検証ギャップなど)を整理し、今後の研究方向と標準化の指針を示した。
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?
4. どうやって有効だと検証した?
5. 議論はある?
6. 次に読むべき論文は?
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著者: Eren Erdal Aksoy, Abu Mohammed Raisuddin, Thien-Thu Ngo, Eren Aydemir, Osman Tahir Ekşi, Paula Litkey, Petri Manninen, Heikki Hyyti, Jyri Maanpää, Tarmo Kivioja, Tuomas Herranen, Väisänen Sofie, Niemi Kalle, Isabelle Morvan, Pierre Duthon, Ian Marsh, Martin Sanfridson, Henrik Toss, Thanh Bui, Yuri Poledna, Maikol Funk Drechsler, Mikko Tarkiainen, Matti Kutila, Topi Miekkala, Stefan Parzefall, Valentina Donzella, Pak Hung Chan, Sogol Kharrazi, Mario Ceccarelli
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Automated driving has gained remarkable momentum over the past decade, with both industry and academia investing substantial resources in developing perception and control solutions to advance vehicle autonomy. However, these efforts have largely focused on clear-weather scenarios, leaving harsh conditions, such as snow, rain, and fog, comparatively understudied. As a result, inclement weather remains one of the most significant hurdles for autonomous vehicles, often leading to substantial performance degradation in downstream perception and control tasks. In this paper, we present insights from the EU-funded Horizon Europe project ROADVIEW, which aims to develop robust, weather-aware perception and control modules for connected and automated vehicles operating under severe conditions, including snow, rain, and fog. Drawing on four years of large-scale development and real-world demonstrations across multiple vehicle platforms in different locations, we identify ten key challenges that hinder the development of weather-resilient autonomy, including data scarcity, domain shifts, limitations in multimodal perception, control robustness, and validation gaps, among others. We summarize these challenges and lessons learned toward enabling reliable snow-autonomous driving, highlighting critical directions for future research and standardization.