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：本文提出一种创新方法来解决自主匝道汇入这一具有挑战性的问题，在该场景中自动驾驶车辆需无缝融入多车道高速公路的车流。我们提出了具备隐状态推断与安全控制器的车道保持与变道智能体（L3IS），该智能体能够在无需完全掌握周围车辆意图或驾驶风格的情况下安全执行匝道汇入任务。我们还提出了该智能体的增强版本AL3IS，其能够处理观测延迟问题，使智能体在存在车对车（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因素进行建模，我们的方法提升了智能体适应动态交通状况、优化汇入操作并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广泛仿真验证了方法的有效性，并与现有方法进行了性能对比。在基于美国101号高速公路真实数据生成的挑战性匝道汇入场景中，L3IS实现了99.90%的成功率。我们进一步对AL3IS进行了敏感性分析，以评估其在变化观测延迟下的鲁棒性，结果表明在1秒V2V通信延迟下仍能保持93.84%的可接受成功率。