Abstract This paper investigates the equity impacts of autonomous vehicles (AV) on for-hire human drivers and passengers in a ride-hailing market, and examines regulation policies that protect human drivers and improve transport equity for ride-hailing passengers. We consider a transportation network companies (TNC) that employs a mixture of AVs and human drivers to provide ride-hailing services. The TNC platform determines the spatial prices, fleet size, human driver payments, and vehicle relocation strategies to maximize its profit, while individual passengers choose between different transport modes to minimize their travel costs. A market equilibrium model is proposed to capture the interactions among passengers, human drivers, AVs, and TNC over the transportation network. The overall problem is formulated as a non-concave program, and an algorithm is developed to derive its approximate solution with a theoretical performance guarantee. Our study shows that TNC prioritizes AV deployment in higher-demand areas to make a higher profit. As AVs flood into these higher-demand areas, they compete with human drivers in the urban core and push them to relocate to suburbs. This leads to reduced earning opportunities for human drivers and increased spatial inequity for passengers. To mitigate these concerns, we consider: (a) a minimum wage for human drivers; and (b) a restrictive pickup policy that prohibits AVs from picking up passengers in higher-demand areas. In the former case, we show that a minimum wage for human drivers will protect them from the negative impact of AVs with negligible impacts on passengers. However, there exists a threshold beyond which the minimum wage will trigger the platform to replace the majority of human drivers with AVs. In the latter case, we show that prohibiting AVs from picking up passengers in higher-demand areas not only improves the spatial equity of ride-hailing services for passengers, but also substantially increases human driver surplus and restricts the increase of total fleet size compared to the unregulated case. These results are validated with realistic case studies for San Francisco.