Communication-aware predictive lane-change coordination for mixed autonomous traffic using SafeLane-VANET

Lane changing is among the most critical operations in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, namely, Vehicular Ad Hoc Networks (VANETs), especially in mixed-traffic scenarios, where autonomous vehicles (AVs) share the road with human-routed and partially connected non-autonomous vehicles (Non-AVs). They are, in general, based on assumptions of instantaneous gap acceptance, heuristic MOBIL-style decision-making, and idealised V2V communication, which may result in delayed conflict resolution, simultaneous lane-change decisions, and reduced manoeuvre stability in dense traffic and under impaired V2V communication. To address these shortcomings, we present SafeLane-VANET, a novel communication-aware framework for predicting safe arbitration behaviour during cooperative lane changes in mixed AV/Non-AV traffic. It combines legality-aware target-lane scoring, V2X-based intent coordination, and short-horizon motion prediction to enable guarded lane changes while accounting for communication uncertainty and the diverse driving behaviour of other road users. To test the framework under various traffic densities, communication conditions, and mixed-fleet ratios, a reproducible SUMO-ns-3-Python co-simulation environment is developed that integrates mobility and packet logging. Experimental test results show that SafeLane-VANET consistently enhances safety, manoeuvre stability, and ride comfort compared to the conventional MOBIL-only case. The proposed framework shows in high-density degraded-communication cases a further improvement of the TTC p5 from 0.95 s to 1.55 s, a reduced percentage of unsafe manoeuvre executions (USM) from 25.1% to 10.4%, a decreased delay p50 from 198 s to 142 s, and a lowered jerk p95 from 4.78 m/s³ to 3.42 m/s³. Further ablation and real-world trajectory validation studies demonstrate that communication-aware intent coordination and predictive safety gating enhance the robustness under mixed-traffic uncertainty. These results show how SafeLane-VANET can be used to improve cooperative autonomy in a safer, more reliable way in a connected vehicular environment. Code is available at https://github.com/NSP310893/SafeLane-VANET.