# Lateral control for autonomous vehicles: A comparative evaluation

arXiv: https://arxiv.org/abs/2311.07987 · HTML: https://mlsanity.com/paper/2311.07987/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2023.

## Built on by (most cited first, 4 of 4)

- [Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey](https://mlsanity.com/paper/2404.00340/) (2024)
- [Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steering](https://mlsanity.com/paper/2409.12716/) (2024)
- [Path Tracking with Dynamic Control Point Blending for Autonomous Vehicles: An Experimental Study](https://mlsanity.com/paper/2602.01892/) (2026)
- [Robust Global-Local Behavior Arbitration via Continuous Command Fusion Under LiDAR Errors](https://mlsanity.com/paper/2603.27273/) (2026)

## Abstract (arXiv)

The selection of an appropriate control strategy is essential for ensuring safe operation in autonomous driving. While numerous control strategies have been developed for specific driving scenarios, a comprehensive comparative assessment of their performance using the same tuning methodology is lacking in the literature. This paper addresses this gap by presenting a systematic evaluation of state-of-the-art model-free and model-based control strategies. The objective is to evaluate and contrast the performance of these controllers across a wide range of driving scenarios, reflecting the diverse needs of autonomous vehicles. To facilitate the comparative analysis, a comprehensive set of performance metrics is selected, encompassing accuracy, robustness, and comfort. The contributions of this research include the design of a systematic tuning methodology, the use of two novel metrics for stability and comfort comparisons and the evaluation through extensive simulations and real tests in an experimental instrumented vehicle over a wide range of trajectories.