DriveVer: Lightweight Trajectory Evaluator as Test-Time Verifier for Autonomous Driving
DriveVer is a lightweight trajectory verifier that enhances planning models on the NAVSIM benchmark.
Key Findings
Methodology
DriveVer employs a dual-head architecture, integrating multi-view visual representations and ego-vehicle kinematic features to predict safety confidence scores and geometric refinement vectors. A dedicated trajectory dataset based on the NAVSIM benchmark is constructed through condition-driven clustering and balanced sampling, providing dense supervision.
Key Results
- DriveVer significantly improves the performance of base planning models on the NAVSIM benchmark, with DiffusionDrive's PDMS increasing by 0.9 points.
- On NAVSIMv2, DriveVer continues to demonstrate stable trajectory optimization capabilities.
- DriveVer introduces minimal computational overhead while maintaining real-time inference efficiency.
Significance
DriveVer breaks the traditional paradigm of improving performance solely through training expansion by enabling trajectory verification and refinement during inference, significantly enhancing the safety and reliability of autonomous driving systems. This approach provides a low-cost performance enhancement path for the autonomous driving field.
Technical Contribution
As a lightweight post-processing framework, DriveVer provides a safety-critical verification stage without modifying the architecture of the underlying planner. It uses a cross-modal cross-attention mechanism to jointly encode candidate trajectories and multi-view visual features, achieving accurate trajectory scoring and geometric refinement.
Novelty
DriveVer is the first to introduce the concept of test-time verification and refinement in autonomous driving, differing from traditional one-shot generation paradigms by enhancing trajectory safety through a lightweight post-processing framework.
Limitations
- DriveVer relies on the NAVSIM dataset and may perform poorly on other datasets.
- Its performance is limited by the quality of the initial trajectory from the base planner.
Future Work
Future work could explore DriveVer's adaptability to different datasets and its integration with other advanced planning models to further enhance performance.
AI Executive Summary
Autonomous driving technology has made significant progress in recent years, but challenges remain in complex scenarios. Existing end-to-end planners often use a one-shot trajectory generation approach, lacking mechanisms for verification and correction during inference, which can lead to unsafe behaviors.
DriveVer is a lightweight trajectory verifier designed to address this issue. It significantly enhances the safety and reliability of autonomous driving systems by verifying and refining initial trajectories during inference. DriveVer employs a dual-head architecture, integrating multi-view visual representations and ego-vehicle kinematic features to predict safety confidence scores and geometric refinement vectors.
Experimental results show that DriveVer significantly improves the performance of base planning models on the NAVSIM benchmark, with DiffusionDrive's PDMS increasing by 0.9 points. DriveVer introduces minimal computational overhead while maintaining real-time inference efficiency, providing a low-cost performance enhancement path for the autonomous driving field.
Deep Analysis
Background
Research in autonomous driving technology has made significant progress in recent years, particularly in the development of end-to-end models. However, these models still face performance bottlenecks when handling complex urban driving scenarios. Existing planners often rely on large-scale training data and model capacity expansion, but this approach incurs high computational costs and diminishing marginal returns as model scale continues to grow.
Core Problem
Existing end-to-end autonomous driving planners typically adopt a one-shot generation paradigm, lacking mechanisms for verification and correction during inference. This approach can lead to suboptimal or unsafe trajectory predictions in complex or adversarial scenarios, limiting the safety and reliability of autonomous driving systems.
Innovation
DriveVer introduces a lightweight test-time verification and refinement framework, breaking the traditional paradigm of improving performance solely through training expansion. It constructs a dedicated trajectory dataset based on the NAVSIM benchmark through condition-driven clustering and balanced sampling, providing dense supervision. A dual-head architecture is employed, integrating multi-view visual representations and ego-vehicle kinematic features to predict safety confidence scores and geometric refinement vectors.
Methodology
- �� Dataset Construction: A dedicated trajectory dataset is constructed based on the NAVSIM benchmark through condition-driven clustering and balanced sampling.
- �� Dual-Head Architecture: Integrates multi-view visual representations and ego-vehicle kinematic features to predict safety confidence scores and geometric refinement vectors.
- �� Lightweight Post-Processing: Provides a safety-critical verification stage without modifying the architecture of the underlying planner.
Experiments
Experiments are conducted on the NAVSIM benchmark, testing with various base planning models such as DiffusionDrive and AdaThinkDrive. PDMS and EPDMS are used as evaluation metrics to verify DriveVer's performance enhancement. Results show that DriveVer significantly improves the performance of base planning models with minimal computational overhead.
Results
DriveVer significantly improves the performance of base planning models on the NAVSIM benchmark, with DiffusionDrive's PDMS increasing by 0.9 points. Results show that DriveVer introduces minimal computational overhead while maintaining real-time inference efficiency, providing a low-cost performance enhancement path for the autonomous driving field.
Applications
DriveVer can be directly applied to existing autonomous driving systems as a lightweight post-processing module to enhance system safety and reliability. It does not require modifications to the base planner, making it widely applicable across different autonomous driving platforms.
Limitations & Outlook
DriveVer relies on the NAVSIM dataset and may perform poorly on other datasets. Its performance is limited by the quality of the initial trajectory from the base planner. Additionally, DriveVer's performance improvement effect may vary across different scenarios.
Plain Language Accessible to non-experts
Imagine driving with a smart assistant in your car. Usually, you follow the navigation directly, but sometimes the road conditions are complex, and you need the assistant to check if the route is safe. DriveVer is like this assistant; it quickly checks if the route is safe after you make a decision and makes adjustments if necessary. This way, even in complex road conditions, you can reach your destination more safely.
ELI14 Explained like you're 14
Imagine you're playing a racing game where your car drives itself. Usually, the car decides how to go based on the game's navigation, but sometimes the road is tricky, and the car might make a not-so-great decision. That's when DriveVer comes in, like a smart assistant. It quickly checks if the route is safe after the car decides and makes adjustments if needed. This way, even on tricky tracks, you can finish the race more safely!
Glossary
DriveVer
DriveVer is a lightweight trajectory verifier used to verify and refine trajectories in autonomous driving systems during inference.
DriveVer integrates multi-view visual representations and ego-vehicle kinematic features to predict safety confidence scores and geometric refinement vectors.
NAVSIM
NAVSIM is a benchmark dataset for autonomous driving research, containing various driving scenarios and conditions.
DriveVer is tested on the NAVSIM benchmark to verify its performance enhancement.
PDMS
PDMS stands for Predictive Driver Model Score, used to evaluate the planning performance of autonomous driving systems.
PDMS is used as an evaluation metric in experiments to verify DriveVer's performance enhancement.
Dual-Head Architecture
A dual-head architecture is a neural network design with two output branches for different tasks.
DriveVer employs a dual-head architecture to integrate multi-view visual representations and ego-vehicle kinematic features, predicting safety confidence scores and geometric refinement vectors.
Condition-Driven Clustering
Condition-driven clustering is a data processing method that groups data based on specific conditions.
DriveVer constructs a dedicated trajectory dataset through condition-driven clustering and balanced sampling.
Open Questions Unanswered questions from this research
- 1 DriveVer's adaptability to different datasets still needs verification, especially in diverse driving scenarios.
- 2 Further research is needed to improve DriveVer's real-time performance and computational efficiency.
Applications
Immediate Applications
Autonomous Driving Systems
DriveVer can be directly integrated into existing autonomous driving systems to enhance trajectory planning safety and reliability.
Long-term Vision
Intelligent Traffic Management
DriveVer's technology can be used in intelligent traffic systems to improve overall traffic safety and efficiency.
Abstract
End-to-end autonomous driving models often encounter performance bottlenecks, as training-time scaling leads to high computational costs and diminishing marginal returns. Existing planners typically adopt a one-shot generation paradigm, lacking secondary validation and active correction mechanisms to detect and revise suboptimal or unsafe trajectories during inference. To address this issue, we propose DriveVer, a lightweight, plug-and-play Test-Time Verifier that leverages the test-time scaling paradigm to enable autonomous driving systems to validate and refine trajectories without costly and heavy training. We construct a dedicated trajectory dataset based on the NAVSIM benchmark through condition-driven clustering and balanced sampling according to ego-vehicle states and navigation commands. Employing a dual-head architecture, DriveVer efficiently fuses candidate trajectories with multi-view visual representations and ego-vehicle kinematic features to simultaneously predict a safety confidence score and an absolute geometric refinement vector. Extensive experiments on the NAVSIM benchmark show that DriveVer significantly improves the performance of base planning models. Notably, as an extremely compact model with only 34M parameters, DriveVer introduces minimal computational overhead, achieving competitive results while maintaining real-time inference efficiency.