Toward Unified Interpretable Decision-Making for Autonomous Driving: A Safety Interval Reserve Approach

The application of autonomous vehicles (AVs) requires a safe and efficient decision-making approach for diverse and complex traffic environments. Most existing methodologies focus on specific scenarios or tasks, and are not sufficiently effective for real-world driving situations. This paper proposes a Safety Interval Reserve (SIR) model to quantify safety time margin, which is inspired by human driving behavior. Concurrently, a SIR network is developed to parametrically delineate the changes of SIR under dynamic traffic conditions. Consequently, a SIR network based decision-making approach (DMA-SIR) is designed to unify the macro path planning and micro behavior decision-making. The macro layer optimizes the global path with considering driving efficiency, while the micro layer generates local driving behavior to ensure safety during decision-making. The DMA-SIR facilitates multi-task management through a unified model and is grounded in motion mechanism. Besides, dataset validation demonstrates the anthropomorphic characteristics of DMA-SIR. As a result, it offers an interpretable method that effectively avoid the black box problem. Finally, extensive simulations experiments are conducted using 48 complex scenarios to verify the performance of DMA-SIR. The results show that DMA-SIR can efficiently handle multi-vehicle scenarios and generate driving trajectory with significant greater efficiency, safety and comfort compared to other methods.