MuSLi: A multi sensor LiDAR detection for C-V2X networks

Abstract

Obstacle detection is a tool adopted in vehicular safety applications, aiming to detect a moving obstacle in an area of interest with the highest accuracy. Different sensors are used for this aim, such as LiDAR devices mounted on board of a vehicle that capture images of the surrounding environment. Extending the number of LiDAR sensors can be useful to improve the obstacle detection accuracy, since multiple images are captured from different distances and directions, and this represents an interesting approach, specially in case of dense networks with cooperative nodes. In this paper we present MuSLi technique, aiming to (i) provide an accurate obstacle detection and (ii) forward alert messages to other cars in the network, in case of correct detection of a pedestrian crossing the street. MuSLi relies on the connected content islands scenario, where each vehicle defined as a content island subscribes to a service in order to receive and share published messages. Specifically, the road safety service allows the detection of an obstacle through multiple LiDAR sensors from neighboring cars. Furthermore, we investigate the fastest transmission mode among those defined in the C-V2X releases to alert the presence of a pedestrian to the other approaching cars. The proposed technique provides the distances by the crossroad in which is better to use V2V, V2I or V2N according to the environment, the scheduling technique and the measured interference.