So-called implicit behavioral cloning with energy-based models has shown promising results in robotic manipulation tasks. We tested if the method's advantages carry on to controlling the steering of a real self-driving car with an end-to-end driving model. We performed an extensive comparison of the implicit behavioral cloning approach with explicit baseline approaches, all sharing the same neural network backbone architecture. Baseline explicit models were trained with regression (MAE) loss, classification loss (softmax and cross-entropy on a discretization), or as mixture density networks (MDN). While models using the energy-based formulation performed comparably to baseline approaches in terms of safety driver interventions, they had a higher whiteness measure, indicating higher jerk. To alleviate this, we show two methods that can be used to improve the smoothness of steering. We confirmed that energy-based models handle multimodalities slightly better than simple regression, but this did not translate to significantly better driving ability. We argue that the steering-only road-following task has too few multimodalities to benefit from energy-based models. This shows that applying implicit behavioral cloning to real-world tasks can be challenging, and further investigation is needed to bring out the theoretical advantages of energy-based models.

翻译：基于能量模型的所谓隐式行为克隆在机器人操控任务中展现出良好前景。我们测试了该方法是否能够将优势延续到使用端到端驾驶模型控制真实自动驾驶汽车的转向。我们对隐式行为克隆方法与显式基线方法进行了广泛比较，所有方法共享相同的神经网络骨干架构。显式基线模型通过回归（平均绝对误差）损失、分类损失（离散化处理后的Softmax与交叉熵）或混合密度网络进行训练。虽然基于能量公式的模型在安全驾驶员干预指标上与基线方法表现相当，但其白度测量值更高，表明加速度变化率更大。为解决此问题，我们展示了两种改善转向平滑度的方法。我们证实能量模型在应对多模态性方面略优于简单回归，但这并未转化为显著更强的驾驶能力。我们论证仅涉及转向的道路跟随任务的多模态性过少，难以从能量模型中获益。这表明将隐式行为克隆应用于现实任务具有挑战性，需进一步研究才能发挥能量模型的理论优势。