On the Supply of Autonomous Vehicles in Platforms
The ongoing large-scale deployment of autonomous vehicle (AV) technology has the potential to fundamentally change the transportation landscape. Due to the high cost of AV hardware, the most likely path to widespread AV use is via platforms that can sustain high utilization, such as ride-hailing and delivery services. Our work studies the incentives of stakeholders in such deployments, possible misalignments, and contracting solutions to overcome them. We consider four potential operational models to commercialize AVs, which we model as a supply chain game between a platform, an AV supplier, and human individual contractors (ICs), including (1) an open platform in which the AV supplier and ICs bring their own vehicles to the system, (2) an AV-supplier operated platform without ICs, (3) a platform that sources AVs through leasing contracts, and (4) an integrated supply chain. We find that except for the AV-only platform, all deployment models are subject to a risk of AV underutilization due to the need to maintain the ICs' utilization sufficiently high to ensure ICs remain engaged. As AV underutilization propagates in a non-integrated supply chain, an open platform can become arbitrarily worse than supply chain integration, and careful usage commitments are needed to overcome the efficiency loss. As such, this paper identifies a new kind of supply chain misalignment that is likely to emerge as AVs become deployed technology, which both platform operators and AV suppliers should be mindful of. Moreover, we demonstrate the value of usage commitment in the deployment of AVs through an open platform, which makes it more efficient than AV-only/AV leasing platforms.
Kamessi Zhao
Kamessi Zhao is a postdoctoral researcher at Waymo's marketplace and supply optimization team. She will join Stanford University’s Graduate School of Business as an Assistant Professor of Operations, Information, and Technology in July 2026. Prior to this, she graduated from the Operations Research Center at MIT, where she was advised by Prof. Daniel Freund. Her research studies how two-sided service platforms, via algorithm and market designs, can incentivize agents' flexibility to enhance operational efficiency. Her PhD works won the Dan and Eva Roos Thesis Prize in 2025 and the MIT Operations Research Center Best Student Paper Award in 2024.