NavSim
13 papers with code • 1 benchmarks • 0 datasets
Data-Driven Non-Reactive Autonomous Vehicle Benchmark.
Evaluates autonomous driving stacks that produce waypoints with a static dataset using the PDM-Score metric. The PDM-score metric performs a pseudo-simulation by rolling out the trajectory and simulating all other actors via log-replay. This results in an open-loop evaluation that correlates with closed-loop performance.
Benchmarks
These leaderboards are used to track progress in NavSim
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find NavSim models and implementationsMost implemented papers
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
autonomousvision/transfuser
•
•
At the time of submission, TransFuser outperforms all prior work on the CARLA leaderboard in terms of driving score by a large margin.
Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
We propose Hydra-MDP, a novel paradigm employing multiple teachers in a teacher-student model.
NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
On a large set of challenging scenarios, we observe that simple methods with moderate compute requirements such as TransFuser can match recent large-scale end-to-end driving architectures such as UniAD.
Planning-oriented Autonomous Driving
opendrivelab/uniad
•
•
CVPR 2023
Oriented at this, we revisit the key components within perception and prediction, and prioritize the tasks such that all these tasks contribute to planning.
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
hustvl/vad
•
•
Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of planning make it challenging.
Enhancing End-to-End Autonomous Driving with Latent World Model
bravegroup/law
•
•
Specifically, our framework \textbf{LAW} uses a LAtent World model to predict future latent features based on the predicted ego actions and the latent feature of the current frame.
DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving
hustvl/diffusiondrive
•
•
CVPR 2025
However, the numerous denoising steps in the robotic diffusion policy and the more dynamic, open-world nature of traffic scenes pose substantial challenges for generating diverse driving actions at a real-time speed.
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving
yvanyin/goalflow
•
•
CVPR 2025
Furthermore, GoalFlow employs an efficient generative method, Flow Matching, to generate multimodal trajectories, and incorporates a refined scoring mechanism to select the optimal trajectory from the candidates.
Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training
Hydra-NeXt surpasses the previous state-of-the-art by 22. 98 DS and 17. 49 SR, marking a significant advancement in autonomous driving.
End-to-End Driving with Online Trajectory Evaluation via BEV World Model
Therefore, we propose an end-to-end driving framework WoTE, which leverages a BEV World model to predict future BEV states for Trajectory Evaluation.
