Abstract
Autonomous driving has received a great deal of attention in the automotive industry and is often seen as the future of transportation. The development of autonomous driving technology has been greatly accelerated by the growth of end-to-end machine learning techniques that have been successfully used for perception, planning, and control tasks. An important aspect of autonomous driving planning is knowing how the environment evolves in the immediate future and taking appropriate actions. An autonomous driving system should effectively use the information collected from the various sensors to form an abstract representation of the world to maintain situational awareness. For this purpose, deep learning models can be used to learn compact latent representations from a stream of incoming data. However, most deep learning models are trained end-to-end and do not incorporate any prior knowledge (e.g., from physics) of the vehicle in the architecture. In this direction, many works have explored physics-infused neural network (PINN) architectures to infuse physics models during training. Inspired by this observation, we present a Kalman filter augmented recurrent neural network architecture to learn the latent representation of the traffic flow using front camera images only. We demonstrate the efficacy of the proposed model in both imitation and reinforcement learning settings using both simulated and real-world datasets. The results show that incorporating an explicit model of the vehicle (states estimated using Kalman filtering) in the end-to-end learning significantly increases performance.
Abstract (translated)
在汽车业中，自动驾驶技术受到了广泛关注，被视为交通运输的未来。自动驾驶技术的发展受到了成功应用于感知、规划和控制任务的终末机器学习技术的快速发展的大大加速。自动驾驶计划的一个重要的方面是了解自然环境在立即 future 中的演变情况，并采取适当的行动。自动驾驶系统应该有效地利用从各种传感器收集的信息，以形成世界抽象表示，维持情境意识。为此，深度学习模型可以用来从 incoming 数据流中学习紧凑的隐态表示。然而，大多数深度学习模型是终末训练的，并且没有将车辆的任何先验知识(例如从物理学)融入架构中。在这方面，许多工作探索了物理学融入神经网络(PINN)架构，在训练期间将物理学模型注入其中。受此观察启发，我们提出了一个卡尔曼滤波增强循环神经网络架构，仅使用前方相机图像学习交通流的隐态表示。我们使用模拟和现实世界数据集证明了该模型的效能，在模仿和强化学习设置中。结果显示，将车辆显式模型(使用卡尔曼滤波估计状态)融入终末学习可以提高性能。
URL
https://arxiv.org/abs/2305.14644
