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Safe Driving of Autonomous Vehicles through State Representation Learning

机译：通过国家代表学习安全驾驶自主车辆

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摘要

In this paper, we propose an environment perception framework for autonomous driving using state representation learning (SRL). Unlike existing Q-learning based methods for efficient environment perception and object detection, our proposed method takes the learning loss into account under deterministic as well as stochastic policy gradient. Through a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC), we focus on uninterrupted and reasonably safe autonomous driving without steering off the track for a considerable driving distance. To ensure the effectiveness of the scheme over a sustained period of time, we employ a reward-penalty based system where a higher negative penalty is associated with an unfavourable action and a comparatively lower positive reward is awarded for favourable actions. The results obtained through simulations on DonKey simulator show the effectiveness of our proposed method by examining the variations in policy loss, value loss, reward function, and cumulative reward for ‘VAE+DDPG’ and ‘VAE+SAC’ over the learning process.
机译：在本文中，我们向使用状态表示学习（SRL）提出了一种用于自主驾驶的环境感知框架。与现有的基于Q学习的基于Q学习的方法不同，我们提出的方法在确定性和随机政策梯度下考虑了学习损失。通过变分性AutoEncoder（VAE），深度确定性政策梯度（DDPG）和软演员 - 评论家（SAC）的组合，我们专注于不间断和合理的自动驾驶，而不会使轨道从轨道上转向，以获得相当大的驾驶距离。为了确保该计划的有效性在持续时间内，我们采用了奖励基于罚款的制度，其中较高的负面惩罚与不利行动有关，奖励相对较低的积极奖励因良好的行动而获得。通过对驴模拟器的模拟获得的结果显示了我们提出的方法的有效性，通过检查了政策丢失，价值损失，奖励功能和“VAE + DDPG”和“VAE + SAC”在学习过程中的累积奖励的变化。

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