Sagar KavaiyaDharmendra ChauhanYoong Choon ChangMohamad Yusoff Bin AliasNguyen Tri HaiBui Minh PhungNarendrakumar ChauhanPurvang Dalal2025-07-312025-07-3120259783031880414978303188042110.1007/978-3-031-88042-1_28https://dspace-cris.utar.edu.my/handle/123456789/11281The 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.enNeuro-ComputingTraffic ManagementConnected CarsAutonomous vehiclesHighway administrationMulti agent systemsAutonomous decisionConnected carDecisions makingsEvolutionary computingEvolutionary frameworkIntelligent computational modelNeural-networksNeurocomputingOptimisationsTraffic managementTraffic controltext::conference output::conference proceedings::conference paper