Cyclist support systems for future traffic: A review

Background: Interpreting the subtleness and complexity of vulnerable road user (VRU) behaviour is still a major challenge for automated vehicles (AVs). Solutions for facilitating safe and acceptable interactions in future automated traffic are equipping AVs and VRUs with human-machine interfaces (HMIs) such as awareness and notifications systems, as well as connecting road users to a network of AVs and infrastructure. Research on these solutions largely focuses on pedestrians. However, to ensure the safety of cyclists in future traffic, targeting cyclists as a specific road user group in research is vital. Cyclists sometimes share lanes with vehicles, and have different speeds, movement patterns, and eye-gazing behaviour than pedestrians.

Currently, there is no overview of the technologies and solutions for cyclists for enhancing the interaction with AVs. The objectives of the present study are to provide an overview of the communicative technologies, systems, and devices available to cyclists, and evaluate how these solutions meet cyclists’ needs in future automated traffic.

Method: To collect relevant academic articles, we performed systematic literature searches in databases such as Scopus, ScienceDirect, and Google Scholar. In addition, we used Google to identify concepts from the industry, including patents and informal concepts. The criterium for the selection of the study sample was set to HMI concepts and communication technologies, where articles not involving cyclists or bicycles were excluded. The study sample was analysed systematically using a taxonomical coding system.

Results: We analysed and coded 69 HMI concepts in four systemic categories: cyclist wearables, on-bike devices, vehicle systems, and infrastructural solutions. The concepts are further differentiated according to physical characteristics, intended functionality, modality of communication, and the technology utilised. The concepts are assessed according to the needs and characteristics of cyclists from a human factors’ perspective. The study is ongoing, and the final results are expected First Quarter 2022.

Conclusion: The findings from this study provide a synthesis of present literature on AV-cyclist interaction and an overview of the state-of-the-art of the cyclist-specific proposed solutions. By evaluating the HMI concepts according to the characteristics of cyclists, we pave the way for future research on safe and acceptable AV-cyclist interaction.