# EvaDrive: Evolutionary Adversarial Policy Optimization for End-to-End Autonomous Driving

arXiv: https://arxiv.org/abs/2508.09158 · HTML: https://mlsanity.com/paper/2508.09158/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 37 of its 55 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)
- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 30 of 37)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023)
- [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022)
- [ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning](https://mlsanity.com/paper/2207.07601/) (2022)
- [NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking](https://mlsanity.com/paper/2406.15349/) (2024)
- [VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning](https://mlsanity.com/paper/2402.13243/) (2024)
- [Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation](https://mlsanity.com/paper/2406.06978/) (2024)
- [Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline](https://mlsanity.com/paper/2206.08129/) (2022)
- [SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation](https://mlsanity.com/paper/2405.19620/) (2024)
- [Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer](https://mlsanity.com/paper/2207.14024/) (2022)
- [Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes](https://mlsanity.com/paper/2305.10430/) (2023)
- [Enhancing End-to-End Autonomous Driving with Latent World Model](https://mlsanity.com/paper/2406.08481/) (2024)
- [UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2](https://mlsanity.com/paper/2012.03539/) (2020)
- [AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning](https://mlsanity.com/paper/2503.07608/) (2025)
- [DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning](https://mlsanity.com/paper/2506.06659/) (2025)
- [DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba](https://mlsanity.com/paper/2408.03601/) (2024)
- [PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving](https://mlsanity.com/paper/1907.04449/) (2019)
- [A Survey on Vision-Language-Action Models for Autonomous Driving](https://mlsanity.com/paper/2506.24044/) (2025)
- [SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving](https://mlsanity.com/paper/2005.03844/) (2020)
- [Generalized Trajectory Scoring for End-to-end Multimodal Planning](https://mlsanity.com/paper/2506.06664/) (2025)
- [Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning](https://mlsanity.com/paper/2506.18234/) (2025)
- [AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving](https://mlsanity.com/paper/2505.15298/) (2025)
- [Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling](https://mlsanity.com/paper/2505.17659/) (2025)

## Built on by (most cited first, 12 of 12)

- [SimScale: Learning to Drive via Real-World Simulation at Scale](https://mlsanity.com/paper/2511.23369/) (2025)
- [Devil is in Narrow Policy: Unleashing Exploration in Driving VLA Models](https://mlsanity.com/paper/2603.06049/) (2026)
- [CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning](https://mlsanity.com/paper/2605.15120/) (2026)
- [DAP: A Discrete-token Autoregressive Planner for Autonomous Driving](https://mlsanity.com/paper/2511.13306/) (2025)
- [DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving](https://mlsanity.com/paper/2603.18315/) (2026)
- [HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving](https://mlsanity.com/paper/2604.03581/) (2026)
- [DiffVLA++: Bridging Cognitive Reasoning and End-to-End Driving through Metric-Guided Alignment](https://mlsanity.com/paper/2510.17148/) (2025)
- [DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving](https://mlsanity.com/paper/2603.19675/) (2026)
- [Post-Training in End-to-End Autonomous Driving](https://mlsanity.com/paper/2607.08072/) (2026)
- [Teaching Vision-Language-Action Models What to See and Where to Look](https://mlsanity.com/paper/2607.01658/) (2026)
- [VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation](https://mlsanity.com/paper/2609.02462/) (2026)
- [DriveReferee: Geometric Safety Verdicts Need Not Be Learned for Driving World-Action Models](https://mlsanity.com/paper/2609.22762/) (2026)

## Abstract (arXiv)

Autonomous driving faces significant challenges in achieving human-like iterative decision-making, which continuously generates, evaluates, and refines trajectory proposals. Current generation-evaluation frameworks isolate trajectory generation from quality assessment, preventing iterative refinement essential for planning, while reinforcement learning methods collapse multi-dimensional preferences into scalar rewards, obscuring critical trade-offs and yielding scalarization bias.To overcome these issues, we present EvaDrive, a novel multi-objective reinforcement learning framework that establishes genuine closed-loop co-evolution between trajectory generation and evaluation via adversarial optimization. EvaDrive frames trajectory planning as a multi-round adversarial game. In this game, a hierarchical generator continuously proposes candidate paths by combining autoregressive intent modeling for temporal causality with diffusion-based refinement for spatial flexibility. These proposals are then rigorously assessed by a trainable multi-objective critic that explicitly preserves diverse preference structures without collapsing them into a single scalarization bias.This adversarial interplay, guided by a Pareto frontier selection mechanism, enables iterative multi-round refinement, effectively escaping local optima while preserving trajectory diversity.Extensive experiments on NAVSIM and Bench2Drive benchmarks demonstrate SOTA performance, achieving 94.9 PDMS on NAVSIM v1 (surpassing DiffusionDrive by 6.8, DriveSuprim by 5.0, and TrajHF by 0.9) and 64.96 Driving Score on Bench2Drive. EvaDrive generates diverse driving styles via dynamic weighting without external preference data, introducing a closed-loop adversarial framework for human-like iterative decision-making, offering a novel scalarization-free trajectory optimization approach.