This paper presents a novel approach to address the challenging problem of autonomous on-ramp merging, where a self-driving vehicle needs to seamlessly integrate into a flow of vehicles on a multi-lane highway. We introduce the Lane-keeping, Lane-changing with Latent-state Inference and Safety Controller (L3IS) agent, designed to perform the on-ramp merging task safely without comprehensive knowledge about surrounding vehicles' intents or driving styles. We also present an augmentation of this agent called AL3IS that accounts for observation delays, allowing the agent to make more robust decisions in real-world environments with vehicle-to-vehicle (V2V) communication delays. By modeling the unobservable aspects of the environment through latent states, such as other drivers' intents, our approach enhances the agent's ability to adapt to dynamic traffic conditions, optimize merging maneuvers, and ensure safe interactions with other vehicles. We demonstrate the effectiveness of our method through extensive simulations generated from real traffic data and compare its performance with existing approaches. L3IS shows a 99.90% success rate in a challenging on-ramp merging case generated from the real US Highway 101 data. We further perform a sensitivity analysis on AL3IS to evaluate its robustness against varying observation delays, which demonstrates an acceptable performance of 93.84% success rate in 1-second V2V communication delay.

翻译：本文提出了一种新颖的方法来解决自主匝道合流的挑战性问题，其中自动驾驶车辆需要无缝融入多车道高速公路的车流中。我们引入了L3IS智能体（车道保持、车道变换与隐状态推断及安全控制器），旨在无需全面了解周围车辆意图或驾驶风格的情况下安全完成匝道合流任务。我们还提出了该智能体的增强版本AL3IS，该版本考虑了观测延迟，使得智能体能够在存在车车（V2V）通信延迟的真实环境中做出更鲁棒的决策。通过隐状态（如其他驾驶员的意图）对环境中的不可观测方面进行建模，我们的方法增强了智能体适应动态交通状况、优化合流操作以及确保与其他车辆安全交互的能力。我们利用真实交通数据生成的大量仿真实验验证了方法的有效性，并将其性能与现有方法进行了比较。在基于美国101号高速公路真实数据生成的具有挑战性的匝道合流案例中，L3IS展示了99.90%的成功率。我们进一步对AL3IS进行了敏感性分析，以评估其在不同观测延迟下的鲁棒性，结果表明在1秒的V2V通信延迟下，其仍能保持93.84%的可接受成功率。