TruckRSS: Adding Variable Mass to the Responsibility-Sensitive Safety Model

The rising number of truck-related fatalities on Brazilian highways, alongside global increases in heavy-duty vehicle (HDV) incidents, highlights the need for advanced adaptive safety systems. Responsibility-Sensitive Safety (RSS) provides a verifiable framework for autonomous driving but struggles with HDV challenges such as highly variable mass. This work proposes TruckRSS, an RSS extension for variable-mass vehicles, integrating mass estimation through Neural Networks (NN) and formal runtime verification using Signal Temporal Logic (STL). A data-driven approach employing Gated Recurrent Unit (GRU) models achieved a Mean Absolute Error (MAE) of 121.91 kg after post-processing. Experimental validation in the CARLA simulator demonstrated that TruckRSS adapts braking behavior based on real-time mass estimation, reducing unnecessary following distances and improving traffic flow efficiency compared to standard RSS. Real-time monitoring evaluations confirmed that TruckRSS can be enforced with minimal computational overhead, supporting its scalability for deployment in safety-critical HDV applications.