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

2025-07-15

AI Summary

Autonomous navigation of two-wheeled vehicles faces significant safety challenges due to their inherent instability, limited actuation power, and high environmental uncertainty in urban settings. Current approaches suffer from critical gaps in perception completeness, real-time safety guarantees, and edge-computing compatibility. Method: This work proposes a lightweight, multi-modal sensor fusion architecture integrated with an edge-optimized deep learning framework, jointly processing visual, IMU, and LiDAR data to achieve robust environment perception and low-latency motion planning under stringent resource constraints. Furthermore, a hierarchical control scheme explicitly tailored to two-wheeled vehicle dynamics is introduced to enhance riding stability and responsive safety in complex scenarios. Contribution/Results: The proposed end-to-end stack delivers a reproducible, deployable technical pathway for intelligent two-wheeled mobility, enabling the development of safe, efficient, and scalable micromobility systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Addressing instability and safety in autonomous two-wheelers
• Bridging perception gaps for reliable autonomous riding systems
• Developing lightweight sensor and AI solutions for micromobility

Innovation

• Multimodal sensor techniques for lightweight platforms
• Edge deep learning architectures for AR systems
• Comprehensive perception systems for AR tasks