Reliable perception remains a key challenge for Connected Automated Vehicles (CAVs) in complex real-world environments, where varying lighting conditions and adverse weather degrade sensing performance. While existing multi-sensor solutions improve local robustness, they remain constrained by limited sensing range, line-of-sight occlusions, and sensor failures on individual vehicles. This paper introduces VALISENS, a validated cooperative perception system that extends multi-sensor fusion beyond a single vehicle through Vehicle-to-Everything (V2X)-enabled collaboration between Connected Automated Vehicles (CAVs) and intelligent infrastructure. VALISENS integrates onboard and roadside LiDARs, radars, RGB cameras, and thermal cameras within a unified multi-agent perception framework. Thermal cameras enhances the detection of Vulnerable Road Users (VRUs) under challenging lighting conditions, while roadside sensors reduce occlusions and expand the effective perception range. In addition, an integrated sensor monitoring module continuously assesses sensor health and detects anomalies before system degradation occurs. The proposed system is implemented and evaluated in a dedicated real-world testbed. Experimental results show that VALISENS improves pedestrian situational awareness by up to 18% compared with vehicle-only sensing, while the sensor monitoring module achieves over 97% accuracy, demonstrating its effectiveness and its potential to support future Cooperative Intelligent Transport Systems (C-ITS) applications.

翻译：在复杂的现实环境中，可靠的感知仍然是网联自动驾驶车辆面临的关键挑战，其中多变的照明条件和恶劣天气会降低传感性能。尽管现有的多传感器解决方案提升了局部鲁棒性，但仍受限于有限的感知范围、视线遮挡以及单车传感器故障。本文提出VALISENS，一种经过验证的协同感知系统，通过网联自动驾驶车辆与智能基础设施之间基于车联网的协作，将多传感器融合扩展至单车之外。VALISENS将车载与路侧激光雷达、毫米波雷达、RGB相机及热成像相机集成于统一的多智能体感知框架中。热成像相机增强了在复杂光照条件下对弱势道路使用者的检测能力，而路侧传感器则减少了遮挡并扩展了有效感知范围。此外，集成式传感器监测模块持续评估传感器状态，并在系统性能下降前检测异常。所提出的系统在专用真实世界测试平台上实现并评估。实验结果表明，与仅依赖车载传感相比，VALISENS将行人态势感知能力提升最高达18%，同时传感器监测模块达到超过97%的准确率，证明了其有效性及其对未来协同智能交通系统应用的支持潜力。