# Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques

arXiv: https://arxiv.org/abs/2306.09462 · HTML: https://mlsanity.com/paper/2306.09462/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 150 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks](https://mlsanity.com/paper/1807.05720/) (2018)
- [Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving](https://mlsanity.com/paper/1612.00147/) (2016)
- [Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios](https://mlsanity.com/paper/2005.02031/) (2020)
- [Control Strategies for Autonomous Vehicles](https://mlsanity.com/paper/2011.08729/) (2020)
- [SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning](https://mlsanity.com/paper/2104.06506/) (2021)
- [TDR-OBCA: A Reliable Planner for Autonomous Driving in Free-Space Environment](https://mlsanity.com/paper/2009.11345/) (2020)
- [Critical ride comfort detection for automated vehicles](https://mlsanity.com/paper/2107.14631/) (2021)
- [Anticipated emotions associated with trust in autonomous vehicles](https://mlsanity.com/paper/2206.09275/) (2022)
- [VeriBlock: A Blockchain-Based Verifiable Trust Management Architecture with Provable Interactions](https://mlsanity.com/paper/2206.05676/) (2022)

## Built on by (most cited first, 2 of 2)

- [StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Driving](https://mlsanity.com/paper/2506.23982/) (2025)
- [Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Driving](https://mlsanity.com/paper/2603.25672/) (2026)

## Abstract (arXiv)

This article outlines the architecture of autonomous driving and related complementary frameworks from the perspective of human comfort. The technical elements for measuring Autonomous Vehicle (AV) user comfort and psychoanalysis are listed here. At the same time, this article introduces the technology related to the structure of automatic driving and the reaction time of automatic driving. We also discuss the technical details related to the automatic driving comfort system, the response time of the AV driver, the comfort level of the AV, motion sickness, and related optimization technologies. The function of the sensor is affected by various factors. Since the sensor of automatic driving mainly senses the environment around a vehicle, including "the weather" which introduces the challenges and limitations of second-hand sensors in autonomous vehicles under different weather conditions. The comfort and safety of autonomous driving are also factors that affect the development of autonomous driving technologies. This article further analyzes the impact of autonomous driving on the user's physical and psychological states and how the comfort factors of autonomous vehicles affect the automotive market. Also, part of our focus is on the benefits and shortcomings of autonomous driving. The goal is to present an exhaustive overview of the most relevant technical matters to help researchers and application developers comprehend the different comfort factors and systems of autonomous driving. Finally, we provide detailed automated driving comfort use cases to illustrate the comfort-related issues of autonomous driving. Then, we provide implications and insights for the future of autonomous driving.