End-to-end driving systems have recently made rapid progress, in particular on CARLA. Independent of their major contribution, they introduce changes to minor system components. Consequently, the source of improvements is unclear. We identify two biases that recur in nearly all state-of-the-art methods and are critical for the observed progress on CARLA: (1) lateral recovery via a strong inductive bias towards target point following, and (2) longitudinal averaging of multimodal waypoint predictions for slowing down. We investigate the drawbacks of these biases and identify principled alternatives. By incorporating our insights, we develop TF++, a simple end-to-end method that ranks first on the Longest6 and LAV benchmarks, gaining 14 driving score over the best prior work on Longest6.

翻译：端到端驾驶系统近年来取得了快速进展，尤其是在CARLA平台上。然而，这些系统的重大贡献往往伴随着对次要系统组件的调整，导致改进来源不明确。我们识别出几乎所有当前最优方法中都存在的两种偏差，它们对CARLA上的性能提升至关重要：(1) 侧向恢复偏差：通过强归纳偏差引导车辆跟踪目标点，以及(2) 纵向平均偏差：对多模态路径点预测进行纵向平均以实现减速。我们深入分析了这些偏差的局限性，并提出了基于原理的替代方案。通过整合我们的研究成果，我们开发了TF++——一种简洁的端到端方法，在Longest6和LAV基准测试中均排名第一，在Longest6上相较于先前最优方法提升了14个驾驶分数。