Enhancing safe mobility through V2V communication: a focus on short-range performance with UWB

Document Type

Article

Publication Title

Discover Applied Sciences

Abstract

In making vehicles autonomous, vehicle-to-vehicle (V2V) communication is essential for enhancing road safety, particularly in preventing collisions. It allows real-time data exchange on vehicle speed, direction, braking behavior, and road conditions among vehicles in the same vicinity. This enables vehicles to overcome failures in their perception system, which is entirely reliable on sensors and sensor data fusion. This study evaluates the performance of three key V2V communication technologies: dedicated short-range communication (DSRC), ultrawideband (UWB), and Cellular Vehicle-to-Everything (C-V2X). Each technology’s capabilities and suitability for short-distance driving scenarios, such as latency, throughput, bandwidth, and packet loss, are examined via simulations on the Python platform. The findings indicate that while C-V2X excels in long-range communication and scalability with 5G support, UWB outperforms in very short ranges (under 30 m) because of its centimeter-level positioning precision and ultralow latency. DSRC is effective in urban environments with established infrastructure, offering low-latency transmission for safety–critical applications. V2V communications also enhance autonomous system decision-making by integrating sensor data with external information, allowing for risk anticipation beyond onboard sensor ranges. The integration of 5G networks with artificial intelligence (AI), machine learning (ML), and edge computing in V2V systems will further advance real-time driving analytics and collision avoidance. This technological synergy is pivotal for autonomous driving and creating a safe, efficient, and connected transportation environment. Ultimately, UWB is ideal for short-range tasks because of its accuracy, whereas C-V2X is suitable for long-distance communication and scalability, positioning it as a key enabler of future intelligent transport systems.

DOI

10.1007/s42452-025-06928-z

Publication Date

5-1-2025

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