A Neuro-Evolutionary Framework for Enhancing Communication, Safety, and Decision-Making in Connected Car Ecosystems

The rapid development of connected car technology imposes vital requirements to develop adaptive and robust intelligent computational models to help enhance communication, safety, and autonomous decision-making for vehicles. This paper deals with how to incorporate neuro-evolutionary computing-the hybrid paradigm of neural networks and evolutionary algorithms-into the connected car ecosystem. We thus propose a holistic framework that harnesses the adaptive learning capabilities of neural networks and the optimization strengths of evolutionary algorithms to ensure optimal real-time decision-making processes for efficient traffic management and safe passage by passengers. The framework houses a multi-agent system, wherein every connected vehicle is an intelligent agent in itself-learning and evolving toward dynamic traffic surroundings. We present simulations and experimental results showing the effectiveness of our neuro-evolutionary approach to reduce communications latencies and lead to better optimization of routes through proactive collision avoidance. From these results, it is suggested that there exists significant promise for neuro-evolutionary computing to address computational challenges in connected car networks, paving the way to safe, resilient, and efficient intelligent transportation systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.