• A hybrid framework combining deep reinforcement learning with rule-based decision-making enhances safety and training efficiency for lane change maneuvers.
• The proposed Risk-fused DDQN algorithm improves convergence reward by up to 7.6% and reduces training episodes by up to 66.8% compared to conventional DDQN.
• Real vehicle tests show a 57.3% improvement in lane change success rate and at least 16.5% increase in time headway, confirming enhanced safety and adaptability.
• The risk assessment model acts as a safety filter, correcting dangerous actions and improving sampling efficiency through a separate experience buffer for dangerous trials.