Modern autonomous driving systems are typically divided into three main tasks: perception, prediction, and planning. The planning task involves predicting the trajectory of the ego vehicle based on inputs from both internal intention and the external environment, and manipulating the vehicle accordingly. Most existing works evaluate their performance on the nuScenes dataset using the L2 error and collision rate between the predicted trajectories and the ground truth. In this paper, we reevaluate these existing evaluation metrics and explore whether they accurately measure the superiority of different methods. Specifically, we design an MLP-based method that takes raw sensor data (e.g., past trajectory, velocity, etc.) as input and directly outputs the future trajectory of the ego vehicle, without using any perception or prediction information such as camera images or LiDAR. Surprisingly, such a simple method achieves state-of-the-art end-to-end planning performance on the nuScenes dataset, reducing the average L2 error by about 30%. We further conduct in-depth analysis and provide new insights into the factors that are critical for the success of the planning task on nuScenes dataset. Our observation also indicates that we need to rethink the current open-loop evaluation scheme of end-to-end autonomous driving in nuScenes. Codes are available at https://github.com/E2E-AD/AD-MLP.

翻译：现代自动驾驶系统通常分为三大任务：感知、预测和规划。规划任务涉及基于内部意图和外部环境的输入预测自车的轨迹，并据此操控车辆。现有大多数方法在nuScenes数据集上使用预测轨迹与真实轨迹之间的L2误差和碰撞率来评估其性能。本文重新审视了这些现有评估指标，并探讨它们是否准确衡量了不同方法的优越性。具体而言，我们设计了一种基于MLP的方法，该方法以原始传感器数据（如历史轨迹、速度等）作为输入，直接输出自车的未来轨迹，而无需任何感知或预测信息（如摄像头图像或激光雷达点云）。令人惊讶的是，这种简单方法在nuScenes数据集上取得了最先进的端到端规划性能，平均L2误差降低了约30%。我们进一步进行了深入分析，并对nuScenes数据集上规划任务成功的关键因素提供了新见解。我们的观察还表明，需要重新思考当前nuScenes中端到端自动驾驶的开环评估方案。代码开源在 https://github.com/E2E-AD/AD-MLP。