Modular design of planning-oriented autonomous driving has markedly advanced end-to-end systems. However, existing architectures remain constrained by an over-reliance on ego status, hindering generalization and robust scene understanding. We identify the root cause as an inherent design within these architectures that allows ego status to be easily leveraged as a shortcut. Specifically, the premature fusion of ego status in the upstream BEV encoder allows an information flow from this strong prior to dominate the downstream planning module. To address this challenge, we propose AdaptiveAD, an architectural-level solution based on a multi-context fusion strategy. Its core is a dual-branch structure that explicitly decouples scene perception and ego status. One branch performs scene-driven reasoning based on multi-task learning, but with ego status deliberately omitted from the BEV encoder, while the other conducts ego-driven reasoning based solely on the planning task. A scene-aware fusion module then adaptively integrates the complementary decisions from the two branches to form the final planning trajectory. To ensure this decoupling does not compromise multi-task learning, we introduce a path attention mechanism for ego-BEV interaction and add two targeted auxiliary tasks: BEV unidirectional distillation and autoregressive online mapping. Extensive evaluations on the nuScenes dataset demonstrate that AdaptiveAD achieves state-of-the-art open-loop planning performance. Crucially, it significantly mitigates the over-reliance on ego status and exhibits impressive generalization capabilities across diverse scenarios.

3D object detection is one of the most important tasks in autonomous driving and robotics. Our research focuses on tackling low efficiency issue of point-based methods, and we propose a novel Instance-Centroid Faster Point Sampling (IC-FPS) framework. We design a Neighboring Feature Diffusion Module (NFDM) to extract local features for the purpose of efficiently distinguishing the foreground from the background. Considering Farthest Point Sampling (FPS) strategy for downsampling is computationally intensive, we propose the Centroid-Instance Sampling Strategy (CISS). CISS samples center point in large-scale point cloud by rapidly sampling the centroid and instance points of the foreground block. The proposed IC-FPS framework can be inserted into every point-based model and effectively replace the first Set Abstraction (SA) layer. Extensive experiments on several public benchmarks demonstrate the superior performance of our proposed IC-FPS. On the Waymo dataset, IC-FPS significantly improves performance of the benchmark model and increases inference speed by 3.8 times. And real-time detection of point-based methods is realized for the first time, which is meaningful for industrial applications.

In the point cloud analysis task, the existing local feature aggregation descriptors (LFAD) do not fully utilize the neighborhood information of center points. Previous methods only use the distance information to constrain the local aggregation process, which is easy to be affected by abnormal points and cannot adequately fit the original geometry of the point cloud. This paper argues that fine-grained geometric information (FGGI) plays an important role in the aggregation of local features. Based on this, we propose a gradient-based local attention module to address the above problem, which is called Gradient Attention Module (GAM). GAM simplifies the process of extracting the gradient information in the neighborhood to explicit representation using the Zenith Angle matrix and Azimuth Angle matrix, which makes the module 35X faster. The comprehensive experiments on the ScanObjectNN dataset, ShapeNet dataset, S3DIS dataset, Modelnet40 dataset, and KITTI dataset demonstrate the effectiveness, efficientness, and generalization of our newly proposed GAM for 3D point cloud analysis. Especially in S3DIS, GAM achieves the highest index in the current point-based model with mIoU/OA/mAcc of 74.4%/90.6%/83.2%.