Accurate behavior anticipation is essential for autonomous vehicles when navigating in close proximity to other vehicles, pedestrians, and cyclists. Thanks to the recent advances in deep learning and inverse reinforcement learning (IRL), we observe a tremendous opportunity to address this need, which was once believed impossible given the complex nature of human decision making. In this article, we summarize the importance of accurate behavior modeling in autonomous driving and analyze the key approaches and major progress that researchers have made, focusing on the potential of deep IRL (D-IRL) to overcome the limitations of previous techniques. We provide quantitative and qualitative evaluations substantiating these observations. Although the field of D-IRL has seen recent successes, its application to model behavior in autonomous driving is largely unexplored. As such, we conclude this article by summarizing the exciting pathways for future breakthroughs.

中文翻译：

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自动驾驶行为预测的深度逆强化学习：车辆运动的准确预测

当自动驾驶汽车靠近其他车辆、行人和骑自行车的人时，准确的行为预测是必不可少的。由于深度学习和逆向强化学习 (IRL) 的最新进展，我们观察到了解决这一需求的巨大机会，鉴于人类决策的复杂性，这曾经被认为是不可能的。在本文中，我们总结了准确行为建模在自动驾驶中的重要性，并分析了研究人员取得的关键方法和主要进展，重点关注深度 IRL (D-IRL) 克服先前技术局限性的潜力。我们提供定量和定性评估来证实这些观察结果。尽管 D-IRL 领域最近取得了成功，其在自动驾驶行为建模中的应用在很大程度上尚未得到探索。因此，我们通过总结未来突破的令人兴奋的途径来结束本文。