# Your Ride, Your Rules: Psychology and Cognition Enabled Automated Driving Systems

arXiv: https://arxiv.org/abs/2506.11842 · HTML: https://mlsanity.com/paper/2506.11842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 22 of its 48 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 22 of 22)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2309.16292/) (2023)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [Language Prompt for Autonomous Driving](https://mlsanity.com/paper/2309.04379/) (2023)
- [Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles](https://mlsanity.com/paper/2309.10228/) (2023)
- [Empowering Autonomous Driving with Large Language Models: A Safety Perspective](https://mlsanity.com/paper/2312.00812/) (2023)
- [MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving](https://mlsanity.com/paper/2301.08595/) (2023)
- [On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation](https://mlsanity.com/paper/2411.11913/) (2024)
- [When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning](https://mlsanity.com/paper/2304.12046/) (2023)
- [Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control](https://mlsanity.com/paper/2309.05115/) (2023)
- [Multi-Sample Long Range Path Planning under Sensing Uncertainty for Off-Road Autonomous Driving](https://mlsanity.com/paper/2403.11298/) (2024)
- [Don't Get Stuck: A Deadlock Recovery Approach](https://mlsanity.com/paper/2408.10167/) (2024)
- [Personalized Lane Change Decision Algorithm Using Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2112.13646/) (2021)
- [Efficient Path Planning in Large Unknown Environments with Switchable System Models for Automated Vehicles](https://mlsanity.com/paper/2310.06974/) (2023)

## Built on by (most cited first, 1 of 1)

- [MILD: Mediator Agent System with Bidirectional Perception and Multi-Layered Alignment for Human-Vehicle Collaboration](https://mlsanity.com/paper/2605.01507/) (2026)

## Abstract (arXiv)

Despite rapid advances in autonomous driving technology, current autonomous vehicles (AVs) lack effective bidirectional human-machine communication, limiting their ability to personalize the riding experience and recover from uncertain or immobilized states. This limitation undermines occupant comfort and trust, potentially hindering the adoption of AV technologies. We propose PACE-ADS (Psychology and Cognition Enabled Automated Driving Systems), a human-centered autonomy framework enabling AVs to sense, interpret, and respond to both external traffic conditions and internal occupant states. PACE-ADS uses an agentic workflow where three foundation model agents collaborate: the Driver Agent interprets the external environment; the Psychologist Agent decodes passive psychological signals (e.g., EEG, heart rate, facial expressions) and active cognitive inputs (e.g., verbal commands); and the Coordinator Agent synthesizes these inputs to generate high-level decisions that enhance responsiveness and personalize the ride. PACE-ADS complements, rather than replaces, conventional AV modules. It operates at the semantic planning layer, while delegating low-level control to native systems. The framework activates only when changes in the rider's psychological state are detected or when occupant instructions are issued. It integrates into existing AV platforms with minimal adjustments, positioning PACE-ADS as a scalable enhancement. We evaluate it in closed-loop simulations across diverse traffic scenarios, including intersections, pedestrian interactions, work zones, and car-following. Results show improved ride comfort, dynamic behavioral adjustment, and safe recovery from edge-case scenarios via autonomous reasoning or rider input. PACE-ADS bridges the gap between technical autonomy and human-centered mobility.