Towards Autonomous Riding: A Review of Perception, Planning, and Control in Intelligent Two-Wheelers
It addresses the need for safe and efficient autonomous riding systems for urban micromobility users, but it is incremental as it builds on existing autonomous driving research.
This review tackles the problem of developing autonomous riding technologies for two-wheeled vehicles by analyzing perception, planning, and control components, identifying critical gaps such as lack of comprehensive perception systems and limited research support.
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.