Imagine-2-Drive: High-Fidelity World Modeling in CARLA for Autonomous Vehicles
TLDR
Proposes Imagine-2-Drive, a WMRL framework with diffusion-based world model and policy for autonomous driving, outperforming baselines in CARLA.
Reasoning
Strengths include novel integration of diffusion models for high-fidelity world modeling and multi-modal policy, addressing error accumulation and decision-making diversity. Weaknesses are evaluation limited to the CARLA simulator without real-world validation, and reliance on simulated benchmarks.
Read-first score
Read-first score 66, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 55.
Field roles
Rank sensitivity
Stability: volatile; rank range: 229.
Keyword Scores
Deep Analysis
Innovations
- DiffDreamer: a diffusion-based world model that generates future observations simultaneously to mitigate error accumulation
- DPA (Diffusion Policy Actor): a diffusion-based policy that models diverse and multi-modal trajectory distributions
- Integration of diffusion-based world model and policy actor into a WMRL framework for sample-efficient autonomous driving
Methodology
Imagine-2-Drive is a World Model-based Reinforcement Learning (WMRL) framework combining a diffusion-based world model (DiffDreamer) that generates future observations in parallel to reduce compounding errors, and a diffusion policy actor (DPA) that captures multi-modal trajectory distributions. The policy is trained within the world model using minimal online interactions, and evaluation is conducted in the CARLA simulator using standard driving benchmarks.
Key Results
The method outperforms prior world model baselines, achieving a 15% improvement in Route Completion and a 20% improvement in Success Rate.