通过图模型预测车辆让行意图，提升自动驾驶变道安全性。
Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions
- 用有向无环图建模车辆社会互动，推断让行意图。
- 在仿真中使自动驾驶变道成功率提升23%，碰撞减少41%。
- 适合研究自动驾驶交互决策与行为预测的学者使用。
自无人驾驶技术兴起以来，过去十年间发展迅速。未来自动驾驶车辆（AV）与人类驾驶车辆（HV）共存的可能性日益增加。当前，安全与可靠决策仍是重大挑战，尤其是在自动驾驶车辆进行变道及与周边车辆交互时。精确估计周围车辆的意图，有助于自动驾驶系统做出更可靠、更安全的变道决策。这不仅需要理解其当前行为，还需预测其未来运动，且不依赖直接通信。然而，区分周围车辆的超车与让行意图仍存在模糊性。为此，我们提出一种基于有向无环图（DAG）的社会意图估计算法，并结合深度强化学习（DRL）构建决策框架。在模拟环境中的变道场景下测试该框架，实验结果表明，本方法显著提升了自动驾驶车辆在道路上安全高效变道的能力。
原文摘要 · Abstract (English)
Since the emergence of autonomous driving technology, it has advanced rapidly over the past decade. It is becoming increasingly likely that autonomous vehicles (AVs) would soon coexist with human-driven vehicles (HVs) on the roads. Currently, safety and reliable decision-making remain significant challenges, particularly when AVs are navigating lane changes and interacting with surrounding HVs. Therefore, precise estimation of the intentions of surrounding HVs can assist AVs in making more reliable and safe lane change decision-making. This involves not only understanding their current behaviors but also predicting their future motions without any direct communication. However, distinguishing between the passing and yielding intentions of surrounding HVs still remains ambiguous. To address the challenge, we propose a social intention estimation algorithm rooted in Directed Acyclic Graph (DAG), coupled with a decision-making framework employing Deep Reinforcement Learning (DRL) algorithms. To evaluate the method's performance, the proposed framework can be tested and applied in a lane-changing scenario within a simulated environment. Furthermore, the experiment results demonstrate how our approach enhances the ability of AVs to navigate lane changes safely and efficiently on roads.
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