Autonomous driving involves complex tasks such as data fusion, object and lane detection, behavior prediction, and path planning. As opposed to the modular approach which dedicates individual subsystems to tackle each of those tasks, the end-to-end approach treats the problem as a single learnable task using deep neural networks, reducing system complexity and minimizing dependency on heuristics. Conditional imitation learning (CIL) trains the end-to-end model to mimic a human expert considering the navigational commands guiding the vehicle to reach its destination, CIL adopts specialist network branches dedicated to learn the driving task for each navigational command. Nevertheless, the CIL model lacked generalization when deployed to unseen environments. This work introduces the conditional imitation co-learning (CIC) approach to address this issue by enabling the model to learn the relationships between CIL specialist branches via a co-learning matrix generated by gated hyperbolic tangent units (GTUs). Additionally, we propose posing the steering regression problem as classification, we use a classification-regression hybrid loss to bridge the gap between regression and classification, we also propose using co-existence probability to consider the spatial tendency between the steering classes. Our model is demonstrated to improve autonomous driving success rate in unseen environment by 62% on average compared to the CIL method.

翻译：自动驾驶涉及数据融合、目标与车道检测、行为预测及路径规划等复杂任务。与采用独立子系统分别处理各项任务的模块化方法不同，端到端方法将问题视为可通过深度神经网络学习的单一任务，从而降低系统复杂度并减少对启发式规则的依赖。条件模仿学习（CIL）通过让端到端模型模仿人类专家驾驶行为进行训练，同时考虑引导车辆抵达目的地的导航指令；CIL采用专用网络分支分别学习不同导航指令对应的驾驶任务。然而，CIL模型在部署至未见环境时泛化能力不足。本研究提出条件模仿协同学习（CIC）方法以解决该问题，该方法通过门控双曲正切单元（GTU）生成的协同学习矩阵，使模型能够学习CIL专用分支间的关联关系。此外，我们将转向回归问题重构为分类任务，采用分类-回归混合损失函数以弥合回归与分类间的差距，并提出利用共存概率来考量转向类别间的空间分布趋势。实验表明，相较于CIL方法，我们的模型在未见环境中的自动驾驶成功率平均提升62%。