Traffic cones are commonly used to mark temporary roads in emergencies like traffic accidents or road control. However, current autonomous driving research focuses mainly on structured roads, with relatively little on unstructured roads. Thus, enabling unmanned vehicles to accurately perceive temporary roads formed by traffic cones in such environments is vital for improving driving safety. This study proposes a deep learning model based on multi-sensor data fusion to detect temporary roads by quickly identifying traffic cones in special scenarios. The framework uses an improved YOLOX model to obtain traffic cones color information via machine vision, then fuses this with the distance data of traffic cones detected by LiDAR to achieve real-time perception of temporary roads. Experimental results show that in unstructured road environments, the model has a 31 ms single-frame detection time and over 85% accuracy within 15 meters. It enables real-time traffic cone detection, meets design goals, and is of great significance for enhancing the driving safety of unmanned vehicles.