Autonomous vehicles (AV) aim to reduce accidents and improve fuel efficiency by leveraging Artificial Intelligence. In mixed traffic, AVs must share the roads with human drivers; thus, they must deal with humans’ unpredictable and sometimes erratic driving. Unfortunately, current AV prototypes have not yet reached these goals and have caused accidents in mixed traffic, highlighting the need for better development and testing methodologies. Current AV testing practices extensively rely on simulations. Testers define, manually or automatically, traffic scenarios and execute them in simulators to expose faults such as collisions or traffic rule violations. However, existing AV tools focus on testing single AV instances or do not consider human drivers. Consequently, they might generate many irrelevant mixed-traffic test scenarios. The Flexcrash platform addresses these issues by allowing the generation and simulation of mixed-traffic scenarios in which multiple AV instances and human drivers participate. By enabling live interactions among them, the Flexcrash platform enables testers to identify realistic critical mixed-traffic scenarios, traffic experts to create new scenarios datasets, and regulators to extend consumer testing benchmarks.