Related papers: Towards Autonomous Riding: A Review of Perception, Planning, and Control in Intelligent Two-Wheelers

Towards Autonomous Riding: A Review of Perception, Planning, and Control in Intelligent Two-Wheelers

URL: http://arxiv.org/abs/2507.11852v1
Date: Wed, 16 Jul 2025 02:33:54 GMT
Title: Towards Autonomous Riding: A Review of Perception, Planning, and Control in Intelligent Two-Wheelers
Authors: Mohammed Hassanin, Mohammad Abu Alsheikh, Carlos C. N. Kuhn, Damith Herath, Dinh Thai Hoang, Ibrahim Radwan,
Abstract summary: The rapid adoption of micromobility solutions has created an urgent need for reliable autonomous riding (AR) technologies.<n>While autonomous driving (AD) systems have matured significantly, AR presents unique challenges due to the inherent instability of two-wheeled platforms.<n>This review aims to accelerate the development of safe, efficient, and scalable autonomous riding systems for future urban mobility.
Score: 9.094915182471803
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The rapid adoption of micromobility solutions, particularly two-wheeled vehicles like e-scooters and e-bikes, has created an urgent need for reliable autonomous riding (AR) technologies. While autonomous driving (AD) systems have matured significantly, AR presents unique challenges due to the inherent instability of two-wheeled platforms, limited size, limited power, and unpredictable environments, which pose very serious concerns about road users' safety. This review provides a comprehensive analysis of AR systems by systematically examining their core components, perception, planning, and control, through the lens of AD technologies. We identify critical gaps in current AR research, including a lack of comprehensive perception systems for various AR tasks, limited industry and government support for such developments, and insufficient attention from the research community. The review analyses the gaps of AR from the perspective of AD to highlight promising research directions, such as multimodal sensor techniques for lightweight platforms and edge deep learning architectures. By synthesising insights from AD research with the specific requirements of AR, this review aims to accelerate the development of safe, efficient, and scalable autonomous riding systems for future urban mobility.

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