Adaptive neuro-fuzzy risk index for safe and interpretable lane-change decisions in Level-3 autonomous driving

Highway lane-change maneuvers pose a safety challenge for SAE Level 3 autonomous driving because conventional threshold-based decisions and isolated surrogate safety measures may inadequately capture the coupling between longitudinal braking demand and evolving inter-vehicle gaps. This study develops an interpretable, risk-aware framework for continuous lane-change assessment during maneuver initiation and execution.

An adaptive neuro-fuzzy inference system (ANFIS) combines normalized longitudinal braking demand and minimum inter-vehicle gap to generate a continuous risk index ranging from zero to one. The index is integrated into a two-phase decision mechanism incorporating multi-vehicle threat assessment, region-dependent maneuver cancellation, quintic Bézier trajectory replanning, and driver takeover supervision. Performance was evaluated through high-fidelity SCANeR/ReScan and MATLAB/Simulink co-simulations against static Mamdani fuzzy, time-to-collision (TTC), deceleration-demand, and time-headway baselines. Robustness was assessed using 1,000 Monte Carlo simulations with variations in vehicle speeds, sensor noise, road friction, and cut-in timing.

Compared with the TTC baseline, the proposed framework increased warning lead time from 0.86 to 1.84 s and minimum two-dimensional clearance from 2.08 to 4.82 m, while reducing maximum deceleration demand from 4.92 to 2.34 m/s ². Mean computational execution time was 1.42 ms, below the 10-ms simulation control-cycle budget. Across the 1,000 randomized simulations, no collisions were observed with the proposed framework; near-miss and false-alarm rates were 0.30% and 1.20%, respectively.

The simulation findings support the potential of continuous neuro-fuzzy risk assessment to provide earlier hazard detection and larger safety margins while retaining rule-based interpretability and low computational cost. Hardware-in-the-loop and real-vehicle testing remain necessary to establish performance beyond the evaluated simulation conditions.