We propose an end-to-end driving model that integrates a multi-task UNet (MTUNet) architecture and control algorithms in a pipeline of data flow from a front camera through this model to driving decisions. It provides quantitative measures to evaluate the holistic, dynamic, and real-time performance of end-to-end driving systems and thus the safety and interpretability of MTUNet. The architecture consists of one segmentation, one regression, and two classification tasks for lane segmentation, path prediction, and vehicle controls. We present three variants of the architecture having different complexities, compare them on different tasks in four static measures for both single and multiple tasks, and then identify the best one by two additional dynamic measures in real-time simulation. Our results show that the performance of the proposed supervised learning model is comparable to that of a reinforcement learning model on curvy roads for the same task, which is not end-to-end but multi-module.

翻译：我们提出了一种端到端驾驶模型，该模型将多任务UNet（MTUNet）架构与控制算法集成于从前置摄像头到驾驶决策的数据流管道中。该模型提供了定量指标来评估端到端驾驶系统的整体性、动态性和实时性能，从而衡量MTUNet的安全性与可解释性。该架构包含一个分割任务、一个回归任务和两个分类任务，分别用于车道分割、路径预测和车辆控制。我们提出了三种复杂度不同的架构变体，在四个静态指标上对比了它们在不同任务中的单任务与多任务性能，并通过实时仿真中的两个额外动态指标确定了最优变体。结果表明，所提出的监督学习模型在弯曲道路上的同类任务中，其性能可与非端到端而是多模块的强化学习模型相媲美。