DT-Drive: a tool for deterministic replay-based testing and debugging of autonomous driving systems

While simulation is essential for testing autonomous driving systems (ADS), high-fidelity simulators like CARLA often exhibit non-determinism, leading to flaky simulation results. We present DT-Drive, a record-modify-replay framework that ensures deterministic ADS evaluation by decoupling the ego vehicle from the recorded driving scenario. This approach enables “ego-injection” for fair multi-ADS comparisons and “counterfactual replay” via environmental modifications (e.g., weather and traffic), facilitating targeted testing and debugging. Validated on 128 CARLA benchmark scenarios, DT-Drive achieved 100% deterministic evaluation results, effectively eliminating simulator-induced flakiness. We also demonstrate its utility for testing and debugging by conducting a controlled comparison of the TransFuser and TransFuser++ agents under identical and modified environmental conditions. DT-Drive and the sample data are available at https://github.com/SanjeethaPennada/DT-Drive. The tool demo video is available at https://youtu.be/HB1DIZSF5E0.