Connected and Automated Vehicles in Mixed Traffic: Sensing, V2X, and Traffic Flow Stability
Keywords:
Connected and automated vehicles, mixed traffic, V2X communication, cooperative perception, traffic flow stability, human–vehicle interaction, intelligent transportation systemsAbstract
This review aims to synthesize and interpret existing research on how sensing, Vehicle-to-Everything (V2X) communication, and human–automation interaction jointly influence traffic flow stability in environments where connected and automated vehicles (CAVs) coexist with human-driven vehicles (HDVs). A qualitative review methodology was employed, focusing exclusively on peer-reviewed literature published between 2015 and 2025. Searches across Scopus, Web of Science, IEEE Xplore, and ScienceDirect identified studies addressing sensing integration, cooperative perception, V2X-enabled control, and stability modeling in mixed traffic. Following relevance screening and quality appraisal, 15 key articles were selected until theoretical saturation was achieved. Data were analyzed thematically using Nvivo 14 software through open, axial, and selective coding, resulting in the identification of five overarching themes: sensing and perception, V2X communication, traffic flow stability, human–vehicle interaction, and system-level integration. Results indicated that multi-sensor fusion and cooperative perception significantly improve situational awareness but remain sensitive to environmental uncertainty and cost constraints. V2X communication—particularly 5G-V2X and edge-based architectures—emerged as essential for synchronization and safety but is hindered by latency, security, and interoperability issues. Traffic flow modeling studies revealed that CAVs enhance string stability and throughput when their penetration rate exceeds a critical threshold, although unpredictable human behaviors can reintroduce oscillations. The analysis further highlighted that human trust calibration and communication transparency strongly affect cooperation and control transitions. Finally, institutional readiness, regulatory coherence, and public education were identified as indispensable for large-scale, stable CAV deployment. CAV integration in mixed traffic requires a multidimensional approach that combines perceptual resilience, secure low-latency communication, adaptive control algorithms, and human-centered policy frameworks. Traffic stability is achieved not through isolated technological advances but through systemic coordination across technical, behavioral, and governance domains.
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