FastB2D: Toward Cost-Efficient Closed-Loop Benchmark for Autonomous Driving

We present FastB2D, a cost-efficient closed-loop benchmark proxy for autonomous driving (AD) development, built on the widely adopted yet costly Bench2Drive. FastB2D reduces the route count by 65% and total evaluation time by over 70%, while estimating Success Rate with a mean absolute error of  1.2% and a Spearman correlation of 0.9+ on held-out advanced AD model variants, and accurately recovering Driving Score, Driving Efficiency, and Driving Smoothness. FastB2D is constructed by addressing two structural inefficiencies we identify in closed-loop AD benchmarks: statistical route redundancy and highly imbalanced per-route evaluation cost, where a small number of long routes can dominate total testing time. Our cost-aware benchmark compression method learns compact route representations directly from model evaluation records and selects representative low-cost anchor routes through cost-aware clustering. Compared with existing general-purpose benchmark compression methods and compact AD benchmarks, FastB2D further reduces evaluation time by 27.2% on average, lowers performance-estimation error by 64% on average, and demonstrates stronger out-of-distribution generalization. All code and data will be released.