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SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation

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arxiv 2405.19620 v2 pith:C3A3ASST submitted 2024-05-30 cs.CV

SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation

Wenchao Sun , Xuewu Lin , Yining Shi , Chuang Zhang , Haoran Wu , Sifa Zheng This is my paper
classification cs.CV
keywords planningsparsesparsedrivedesigndrivingend-to-endautonomousmodule
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The well-established modular autonomous driving system is decoupled into different standalone tasks, e.g. perception, prediction and planning, suffering from information loss and error accumulation across modules. In contrast, end-to-end paradigms unify multi-tasks into a fully differentiable framework, allowing for optimization in a planning-oriented spirit. Despite the great potential of end-to-end paradigms, both the performance and efficiency of existing methods are not satisfactory, particularly in terms of planning safety. We attribute this to the computationally expensive BEV (bird's eye view) features and the straightforward design for prediction and planning. To this end, we explore the sparse representation and review the task design for end-to-end autonomous driving, proposing a new paradigm named SparseDrive. Concretely, SparseDrive consists of a symmetric sparse perception module and a parallel motion planner. The sparse perception module unifies detection, tracking and online mapping with a symmetric model architecture, learning a fully sparse representation of the driving scene. For motion prediction and planning, we review the great similarity between these two tasks, leading to a parallel design for motion planner. Based on this parallel design, which models planning as a multi-modal problem, we propose a hierarchical planning selection strategy , which incorporates a collision-aware rescore module, to select a rational and safe trajectory as the final planning output. With such effective designs, SparseDrive surpasses previous state-of-the-arts by a large margin in performance of all tasks, while achieving much higher training and inference efficiency. Code will be avaliable at https://github.com/swc-17/SparseDrive for facilitating future research.

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Cited by 14 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MOJITO: Modal Joint Learning for Unified End-to-End Autonomous Driving

    cs.CV 2026-07 conditional novelty 6.0

    Block-wise Modal Joint Attention over image, LiDAR, and diffusion action tokens yields 88.9 PDMS / 88.4 EPDMS on NAVSIM without anchors or auxiliary supervision.

  2. Temporal Sampling Frequency Matters: A Capacity-Aware Study of End-to-End Driving Trajectory Prediction

    cs.CV 2026-05 unverdicted novelty 6.0

    Smaller end-to-end autonomous driving models achieve optimal 3-second trajectory prediction accuracy at lower or intermediate temporal sampling frequencies, whereas larger VLA-style models perform best at the highest ...

  3. Unified Map Prior Encoder for Mapping and Planning

    cs.CV 2026-05 unverdicted novelty 6.0

    UMPE fuses any subset of HD/SD vector maps, raster SD maps, and satellite imagery into BEV features via alignment-aware vector and raster branches, raising mapping mAP by 5.3-5.9 points and cutting planning L2 error b...

  4. Leveraging Previous-Traversal Point Cloud Map Priors for Camera-Based 3D Object Detection and Tracking

    cs.CV 2026-04 unverdicted novelty 6.0

    DualViewMapDet fuses prior-traversal point cloud maps into camera features via dual perspective-view and bird's-eye-view encoding to improve 3D detection and tracking without LiDAR.

  5. AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving

    cs.RO 2026-01 unverdicted novelty 6.0

    A cascaded end-to-end driving model conditions longitudinal planning on the lateral path via anchor-based regression and path-conditioned 1D displacement prediction, achieving SOTA driving score of 89.07 and 73.18% su...

  6. AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving

    cs.RO 2026-01 conditional novelty 6.0

    Conditioning speed planning on the predicted path and relabeling synthetic cut-ins yields SOTA Bench2Drive scores (DS 89.07, SR 73.18%).

  7. AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning

    cs.CV 2025-06 unverdicted novelty 6.0

    AutoVLA unifies semantic reasoning and trajectory planning in one autoregressive VLA model for end-to-end autonomous driving by tokenizing trajectories into discrete actions and using GRPO reinforcement fine-tuning to...

  8. CADET: Physics-Grounded Causal Auditing and Training-Free Deconfounding of End-to-End Driving Planners

    cs.RO 2026-06 unverdicted novelty 5.0

    CADET is a training-free framework for auditing, benchmarking, and repairing spurious correlations in pretrained end-to-end autonomous driving planners using physics-grounded causal methods.

  9. SparseWorld: Enhancing End-to-End Autonomous Driving via World Models with Sparse Scene Representation

    cs.CV 2026-05 unverdicted novelty 5.0

    SparseWorld is a sparse world model with a Sparse Dreamer module that performs autoregressive rollout of future instances to refine motion prediction and planning, reporting 0.05% collision rate on nuScenes open-loop metrics.

  10. Radar-Camera BEV Multi-Task Learning with Cross-Task Attention Bridge for Joint 3D Detection and Segmentation

    cs.CV 2026-04 unverdicted novelty 5.0

    CTAB exchanges features between detection and segmentation via multi-scale deformable attention in BEV space, yielding segmentation gains on 7 nuScenes classes at neutral detection cost.

  11. Radar-Camera BEV Multi-Task Learning with Cross-Task Attention Bridge for Joint 3D Detection and Segmentation

    cs.CV 2026-04 conditional novelty 5.0

    CTAB bidirectional deformable attention between detection and segmentation BEV branches improves 7-class mIoU by 0.6 at neutral detection on nuScenes radar-camera multi-task learning.

  12. DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving

    cs.CV 2025-07 unverdicted novelty 5.0

    DIVER uses RL-guided diffusion to produce diverse feasible trajectories from one ground-truth path, addressing mode collapse in imitation learning for autonomous driving.

  13. FocalAD: Local Motion Planning for End-to-End Autonomous Driving

    cs.AI 2025-06 conditional novelty 5.0

    FocalAD adds an ego-local graph interactor and focal loss to prioritize decision-critical neighbors, yielding lower collision rates than prior methods on nuScenes, Bench2Drive, and especially the Adv-nuScenes robustness set.

  14. DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving

    cs.CV 2026-05 unverdicted novelty 4.0

    DeepSight uses parallel latent feature prediction in BEV for long-horizon world modeling and adaptive text reasoning to reach state-of-the-art closed-loop performance on the Bench2drive benchmark.