pith. sign in

arxiv: 2303.12077 · v3 · pith:F6JYQNSInew · submitted 2023-03-21 · 💻 cs.RO · cs.CV

VAD: Vectorized Scene Representation for Efficient Autonomous Driving

Bo Jiang , Shaoyu Chen , Qing Xu , Bencheng Liao , Jiajie Chen , Helong Zhou , Qian Zhang , Wenyu Liu
show 2 more authors
Chang Huang Xinggang Wang
This is my paper
classification 💻 cs.RO cs.CV
keywords planningdrivingvectorizedautonomousrepresentationend-to-endperformanceprevious
0
0 comments X
read the original abstract

Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene representation (e.g., agent occupancy and semantic map) to perform planning, which is computationally intensive and misses the instance-level structure information. In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation. The proposed vectorized paradigm has two significant advantages. On one hand, VAD exploits the vectorized agent motion and map elements as explicit instance-level planning constraints which effectively improves planning safety. On the other hand, VAD runs much faster than previous end-to-end planning methods by getting rid of computation-intensive rasterized representation and hand-designed post-processing steps. VAD achieves state-of-the-art end-to-end planning performance on the nuScenes dataset, outperforming the previous best method by a large margin. Our base model, VAD-Base, greatly reduces the average collision rate by 29.0% and runs 2.5x faster. Besides, a lightweight variant, VAD-Tiny, greatly improves the inference speed (up to 9.3x) while achieving comparable planning performance. We believe the excellent performance and the high efficiency of VAD are critical for the real-world deployment of an autonomous driving system. Code and models are available at https://github.com/hustvl/VAD for facilitating future research.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

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

  1. DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

    cs.CV 2024-02 unverdicted novelty 6.0

    DriveVLM adds vision-language models with scene description, analysis, and hierarchical planning modules to autonomous driving, paired with a hybrid DriveVLM-Dual system tested on nuScenes and SUP-AD datasets and depl...

  2. GPT-Driver: Learning to Drive with GPT

    cs.CV 2023-10 conditional novelty 6.0

    GPT-3.5 is turned into an autonomous-vehicle motion planner by representing driving scenes and trajectories as language tokens and applying a prompting-reasoning-finetuning pipeline, with results shown on nuScenes.

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

    cs.CV 2023-05 accept novelty 6.0

    A perception-free MLP reduces average L2 trajectory error by ~20% versus perception-based methods on nuScenes, suggesting current open-loop evaluation may reward trajectory mimicry over safe planning.

  4. Latency Analysis and Optimization of Alpamayo 1 via Efficient Trajectory Generation

    cs.AI 2026-05 unverdicted novelty 5.0

    Redesigning Alpamayo 1 to single-reasoning and optimizing diffusion action generation cuts inference latency by 69.23% while preserving trajectory diversity and prediction quality.

  5. InterFuserDVS: Event-Enhanced Sensor Fusion for Safe RL-Based Decision Making

    cs.CV 2026-05 unverdicted novelty 5.0

    Integrating DVS event data into InterFuser through token fusion yields a driving score of 77.2 and 100% route completion on CARLA benchmarks, indicating improved robustness in dynamic conditions.