Autonomous Vehicles (AVs), furnished with sensors capable of capturing essential vehicle dynamics such as speed, acceleration, and precise location, possess the capacity to execute intelligent maneuvers, including lane changes, in anticipation of approaching roadblocks. Nevertheless, the sheer volume of sensory data and the processing necessary to derive informed decisions can often overwhelm the vehicles, rendering them unable to handle the task independently. Consequently, a common approach in traffic scenarios involves transmitting the data to servers for processing, a practice that introduces challenges, particularly in situations demanding real-time processing. In response to this challenge, we present a novel DL-based semantic traffic control system that entrusts semantic encoding responsibilities to the vehicles themselves. This system processes driving decisions obtained from a Reinforcement Learning (RL) agent, streamlining the decision-making process. Specifically, our framework envisions scenarios where abrupt roadblocks materialize due to factors such as road maintenance, accidents, or vehicle repairs, necessitating vehicles to make determinations concerning lane-keeping or lane-changing actions to navigate past these obstacles. To formulate this scenario mathematically, we employ a Markov Decision Process (MDP) and harness the Deep Q Learning (DQN) algorithm to unearth viable solutions.

翻译：自动驾驶车辆配备能够捕获关键车辆动态（如速度、加速度和精确位置）的传感器，具备执行智能操作（包括变道）以应对临近路障的能力。然而，海量的传感数据及制定决策所需的处理负荷常使车辆不堪重负，导致其无法独立完成任务。因此，交通场景中通常采用将数据传输至服务器处理的方案，但这一做法尤其在需要实时处理的情境中会带来挑战。针对此问题，我们提出了一种基于深度学习的新型语义交通控制系统，该系统将语义编码任务交由车辆自身承担。本系统通过处理从强化学习智能体获得的驾驶决策，从而简化决策流程。具体而言，我们的框架针对因道路养护、事故或车辆维修等因素导致突发路障的场景，要求车辆做出保持车道或变换车道的决策以绕过障碍。为对该场景进行数学建模，我们采用马尔可夫决策过程，并利用深度Q学习算法来探索可行的解决方案。