In autonomous driving, the end-to-end (E2E) driving approach that predicts vehicle control signals directly from sensor data is rapidly gaining attention. To learn a safe E2E driving system, one needs an extensive amount of driving data and human intervention. Vehicle control data is constructed by many hours of human driving, and it is challenging to construct large vehicle control datasets. Often, publicly available driving datasets are collected with limited driving scenes, and collecting vehicle control data is only available by vehicle manufacturers. To address these challenges, this letter proposes the first fully self-supervised learning framework, self-supervised imitation learning (SSIL), for E2E driving, based on the self-supervised regression learning framework. The proposed SSIL framework can learn E2E driving networks without using driving command data. To construct pseudo steering angle data, proposed SSIL predicts a pseudo target from the vehicle's poses at the current and previous time points that are estimated with light detection and ranging sensors. In addition, we propose two modified E2E driving networks that predict driving commands depending on high-level instruction. Our numerical experiments with three different benchmark datasets demonstrate that the proposed SSIL framework achieves very comparable E2E driving accuracy with the supervised learning counterpart.

翻译：在自动驾驶领域，能够直接从传感器数据预测车辆控制信号的端到端驾驶方法正迅速受到关注。学习安全的端到端驾驶系统需要大量驾驶数据与人工介入。车辆控制数据需通过数小时人工驾驶构建，构建大规模车辆控制数据集具有挑战性。现有公开驾驶数据集通常采集场景有限，且车辆控制数据往往仅由汽车制造商掌握。为应对这些挑战，本文基于自监督回归学习框架，首次提出用于端到端驾驶的完全自监督学习框架——自监督模仿学习。所提出的SSIL框架可在无需驾驶指令数据的情况下学习端到端驾驶网络。为构建伪方向盘转角数据，SSIL通过激光雷达传感器估计的当前与前一时刻车辆位姿预测伪目标值。此外，我们提出两种改进的端到端驾驶网络，能够根据高级指令预测驾驶命令。在三个不同基准数据集上的数值实验表明，所提出的SSIL框架实现了与监督学习方法高度相当的端到端驾驶精度。