# FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback

arXiv: https://arxiv.org/abs/2411.18013 · HTML: https://mlsanity.com/paper/2411.18013/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Chain-of-thought reasoning for driving, one of 17 directions in LLM reasoning.
- Builds on 19 of its 63 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Placed in

- LLM reasoning / [Chain-of-thought reasoning for driving](https://mlsanity.com/lr/chain-of-thought-reasoning-for-driving/)
- End-to-end autonomous driving / [Reasoning VLA and VLM driving policies](https://mlsanity.com/driving/reasoning-vla-and-vlm-driving-policies/)

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [DriveLM: Driving with Graph Visual Question Answering](https://mlsanity.com/paper/2312.14150/) (2023)
- [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning](https://mlsanity.com/paper/2402.13243/) (2024)
- [DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving](https://mlsanity.com/paper/2312.09245/) (2023)
- [GenAD: Generative End-to-End Autonomous Driving](https://mlsanity.com/paper/2402.11502/) (2024)
- [Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer](https://mlsanity.com/paper/2207.14024/) (2022)
- [A Language Agent for Autonomous Driving](https://mlsanity.com/paper/2311.10813/) (2023)
- [DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving](https://mlsanity.com/paper/2403.16996/) (2024)
- [RL-GPT: Integrating Reinforcement Learning and Code-as-policy](https://mlsanity.com/paper/2402.19299/) (2024)
- [Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction](https://mlsanity.com/paper/2402.15368/) (2024)
- [Instruct Large Language Models to Drive like Humans](https://mlsanity.com/paper/2406.07296/) (2024)
- [Tackling Hate Speech in Low-resource Languages with Context Experts](https://mlsanity.com/paper/2303.16828/) (2023)
- [Tell me, what are you most afraid of? Exploring the Effects of Agent Representation on Information Disclosure in Human-Chatbot Interaction](https://mlsanity.com/paper/2307.12345/) (2023)

## Built on by (most cited first, 18 of 18)

- [Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future](https://mlsanity.com/paper/2512.16760/) (2025)
- [ReAL-AD: Towards Human-Like Reasoning in End-to-End Autonomous Driving](https://mlsanity.com/paper/2507.12499/) (2025)
- [EvaDrive: Evolutionary Adversarial Policy Optimization for End-to-End Autonomous Driving](https://mlsanity.com/paper/2508.09158/) (2025)
- [MindDrive: An All-in-One Framework Bridging World Models and Vision-Language Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2512.04441/) (2025)
- [MTRDrive: Memory-Tool Synergistic Reasoning for Robust Autonomous Driving in Corner Cases](https://mlsanity.com/paper/2509.20843/) (2025)
- [CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving](https://mlsanity.com/paper/2505.21581/) (2025)
- [FSD-VLN: Fast-Slow Dual-System Modeling for Aerial Long-Horizon Vision-Language Navigation](https://mlsanity.com/paper/2607.08359/) (2026)
- [Run, Ruminate, and Regulate: A Dual-process Thinking System for Vision-and-Language Navigation](https://mlsanity.com/paper/2511.14131/) (2025)
- [Smooth Operator: Smooth Verifiable Reward Activates Spatial Reasoning Ability of Vision-Language Model](https://mlsanity.com/paper/2601.07695/) (2026)
- [XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments](https://mlsanity.com/paper/2604.18484/) (2026)
- [BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving](https://mlsanity.com/paper/2606.08684/) (2026)
- [ASSCG: Just-Right Gating over Chattering for Fast-Slow LLM Planning in Autonomous Driving](https://mlsanity.com/paper/2606.25509/) (2026)
- [4DSegStreamer: Streaming 4D Panoptic Segmentation via Dual Threads](https://mlsanity.com/paper/2510.17664/) (2025)
- [SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screening](https://mlsanity.com/paper/2605.17610/) (2026)
- [Cognitive Dual-Process Planning for Autonomous Driving with Structured Scene Knowledge and Verifiable Reasoning-Action Consistency](https://mlsanity.com/paper/2607.19194/) (2026)
- [Decoupling Planning and Control for Instructable Agents](https://mlsanity.com/paper/2608.26788/) (2026)
- [Beyond Textual Chain-of-Thought: A Survey on Action-Grounded Reasoning in Autonomous Driving](https://mlsanity.com/paper/2609.01659/) (2026)
- [Drive-HWM: Hierarchical World Models for Dynamic-Latent Guided Autonomous Driving](https://mlsanity.com/paper/2609.03572/) (2026)

## Abstract (arXiv)

Ensuring safe, comfortable, and efficient navigation is a critical goal for autonomous driving systems. While end-to-end models trained on large-scale datasets excel in common driving scenarios, they often struggle with rare, long-tail events. Recent progress in large language models (LLMs) has introduced enhanced reasoning capabilities, but their computational demands pose challenges for real-time decision-making and precise planning. This paper presents FASIONAD, a novel dual-system framework inspired by the cognitive model "Thinking, Fast and Slow." The fast system handles routine navigation tasks using rapid, data-driven path planning, while the slow system focuses on complex reasoning and decision-making in challenging or unfamiliar situations. A dynamic switching mechanism based on score distribution and feedback allows seamless transitions between the two systems. Visual prompts generated by the fast system enable human-like reasoning in the slow system, which provides high-quality feedback to enhance the fast system's decision-making. To evaluate FASIONAD, we introduce a new benchmark derived from the nuScenes dataset, specifically designed to differentiate fast and slow scenarios. FASIONAD achieves state-of-the-art performance on this benchmark, establishing a new standard for frameworks integrating fast and slow cognitive processes in autonomous driving. This approach paves the way for more adaptive, human-like autonomous driving systems.