The field of autonomous driving systems is moving towards more robust and transferable validation methods, with a focus on closing the reality gap between simulated and real-world behavior. Researchers are exploring various testing modalities, including simulation-based testing, mixed-reality testing, and real-world testing, to improve the accuracy and reliability of autonomous driving systems. The development of closed-loop evaluation frameworks and benchmarks is also a key area of research, with a focus on creating more realistic and challenging scenarios for testing autonomous driving models. Additionally, studies are investigating the effectiveness of driver training interventions in improving safe engagement with vehicle automation systems, and the formation of trust in autonomous vehicles through real-world interactions. Noteworthy papers include: A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems, which presents a comprehensive empirical study comparing four representative testing modalities. DriveE2E: Closed-Loop Benchmark for End-to-End Autonomous Driving through Real-to-Simulation, which introduces a simple yet challenging closed-loop evaluation framework that closely integrates real-world driving scenarios into the CARLA simulator.