TakeoverBench: A Benchmarking Platform for Takeover Requests (TOR) in Conditionally Automated Vehicles

Abstract

As autonomous vehicle technologies advance, increasing portions of driving control are shifting from human drivers to automated systems. Takeover situations, where control must be safely returned to the driver, remain a critical focus for safety evaluation. These handovers depend on effective takeover requests, yet existing studies are difficult to compare because they use different simulator setups, scenario designs, and performance measures. This fragmentation creates a need for a consistent workflow for designing, running, and reporting takeover experiments. In addition, developing and testing human-machine interface designs can be resource-intensive, limiting rapid comparison of alternative alert presentations. To address these limitations, we introduce TakeoverBench, an open-source benchmarking platform for autonomous vehicle takeover research. The platform integrates scenario execution, takeover alert triggering, human-machine interface testing, telemetry capture, hardware documentation, and automated report generation. Our demonstration shows how these components support consistent experimental conditions, reduce manual data-processing effort, and improve comparison across research setups.