论文标题
意外风险自适应V2X网络的强化学习
Reinforcement Learning for Accident Risk-Adaptive V2X Networking
论文作者
论文摘要
随着连接和自动驾驶汽车在实践中的发展，车辆到全能（V2X）通信的重要性已越来越大。关键挑战是动态性：每辆车都需要识别周围环境的频繁变化并将其应用于其网络行为。这是强调机器学习的重点。但是，由于动态性，学习本身也极为复杂，这必须使学习框架本身必须根据环境具有弹性和灵活性。因此，本文提出了一个V2X网络框架，将增强学习（RL）集成到多个访问的计划中。具体而言，学习机制被称为多臂强盗（MAB）问题，该问题使车辆在没有外部基础设施的任何帮助的情况下可以（i）学习环境，（ii）量化事故风险，（iii）根据风险调整其退缩柜台。本文的结果表明，提出的学习方案能够（i）评估接近最佳的事故风险，并且（ii）因此，危险车辆的传输机会更高。
The significance of vehicle-to-everything (V2X) communications has been ever increased as connected and autonomous vehicles get more emergent in practice. The key challenge is the dynamicity: each vehicle needs to recognize the frequent changes of the surroundings and apply them to its networking behavior. This is the point where the need for machine learning is highlighted. However, the learning itself is extremely complicated due to the dynamicity as well, which necessitates that the learning framework itself must be resilient and flexible according to the environment. As such, this paper proposes a V2X networking framework integrating reinforcement learning (RL) into scheduling of multiple access. Specifically, the learning mechanism is formulated as a multi-armed bandit (MAB) problem, which enables a vehicle, without any assistance from external infrastructure, to (i) learn the environment, (ii) quantify the accident risk, and (iii) adapt its backoff counter according to the risk. The results of this paper show that the proposed learning protocol is able to (i) evaluate an accident risk close to optimal and (ii) as a result, yields a higher chance of transmission for a dangerous vehicle.
