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 this https URL

本论文重新评估了自动驾驶系统在三个主要任务中规划任务的评估方法，并设计了一种基于 MLP 的方法，仅使用原始传感器数据直接输出自车未来轨迹，在 nuScenes 数据集上实现了最先进的端到端规划性能，降低了约 30％的平均 L2 错误。同时，提供了对 nuScenes 数据集成功规划任务的关键因素进行深入分析，并提出了需要重新思考的问题。

重新思考在 NuScenes 中终端到终端自主驾驶的开环评估

Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in  nuScenes

The task of Group Activity Recognition (GAR) aims to predict the activity
category of the group by learning the actor spatial-temporal interaction
relation in the group. Therefore, an effective actor relation learning method
is crucial for the GAR task. The previous works mainly learn the interaction
relation by the well-designed GCNs or Transformers. For example, to infer the
actor interaction relation, GCNs need a learnable adjacency, and Transformers
need to calculate the self-attention. Although the above methods can model the
interaction relation effectively, they also increase the complexity of the
model (the number of parameters and computations). In this paper, we design a
novel MLP-based method for Actor Interaction Relation learning (MLP-AIR) in
GAR. Compared with GCNs and Transformers, our method has a competitive but
conceptually and technically simple alternative, significantly reducing the
complexity. Specifically, MLP-AIR includes three sub-modules: MLP-based Spatial
relation modeling module (MLP-S), MLP-based Temporal relation modeling module
(MLP-T), and MLP-based Relation refining module (MLP-R). MLP-S is used to model
the spatial relation between different actors in each frame. MLP-T is used to
model the temporal relation between different frames for each actor. MLP-R is
used further to refine the relation between different dimensions of relation
features to improve the feature's expression ability. To evaluate the MLP-AIR,
we conduct extensive experiments on two widely used benchmarks, including the
Volleyball and Collective Activity datasets. Experimental results demonstrate
that MLP-AIR can get competitive results but with low complexity.

该论文提出了一种 MLP-AIR 方法用于在团体活动识别任务中学习演员空间和时间互动关系，相较于 GCNs 和 Transformers 的方法，该方法能够获得不错的结果，但具备低复杂度。