On the Role of Sensor Fusion for Object Detection in Future Vehicular Networks

TL;DR

This paper evaluates how combining different sensors like cameras and LiDAR affects object detection in autonomous vehicles, aiming to optimize data sharing with minimal accuracy loss.

Contribution

It extends an object detection algorithm to incorporate multiple sensor inputs and compares their effectiveness using realistic datasets.

Findings

Sensor fusion improves detection accuracy.

LiDAR-only inputs can match large object detection performance.

Fusion reduces data transmission needs.

Abstract

Fully autonomous driving systems require fast detection and recognition of sensitive objects in the environment. In this context, intelligent vehicles should share their sensor data with computing platforms and/or other vehicles, to detect objects beyond their own sensors' fields of view. However, the resulting huge volumes of data to be exchanged can be challenging to handle for standard communication technologies. In this paper, we evaluate how using a combination of different sensors affects the detection of the environment in which the vehicles move and operate. The final objective is to identify the optimal setup that would minimize the amount of data to be distributed over the channel, with negligible degradation in terms of object detection accuracy. To this aim, we extend an already available object detection algorithm so that it can consider, as an input, camera images, LiDAR point clouds, or a combination of the two, and compare the accuracy performance of the different approaches using two realistic datasets. Our results show that, although sensor fusion always achieves more accurate detections, LiDAR only inputs can obtain similar results for large objects while mitigating the burden on the channel.