A naturalistic bicycling study in the Ann Arbor area
Project Abstract/Statement of Work:
In the past few years much progress have been made in the self-driving technologies and related issues (e.g., legislation and regulation) by a variety of entities from automotive and tech industries, academic institutions, and government and organizations. However, there are still great challenges to be solved. One of the critical challenges is that the self-driving cars need to share the existing infrastructure with other non-motorized road users such as bicyclists and pedestrians. Given the complexity of the real-world road environment and the presumably high variability of the behaviors of the non-motorized road users, how the self-driving cars should be designed, tested, and tuned to share the road with bicyclists and pedestrians in a safe and efficient manner is a complicated and yet crucial question. One way to potentially help answer this question is to collect naturalistic data of people riding bicycles in their everyday trips on real-world roadways, and use the collected quantitative data to create guidelines, supports, and test scenarios to develop the artificial intelligence algorithms for self-driving cars in their ability to effectively interact with bicyclists in real-world environment.