Efficient Object Detection Using Quasi-2D LiDAR Data for Autonomous Vehicles

Accurate object detection is important for the safe navigation of autonomous vehicles (AVs) in dynamic environments. Conventional AVs rely on high-resolution but bulky, topmounted 3D Light Detection And Ranging (LiDAR) sensors, which are impractical for scalable production and computationally expensive to process. To address this, we utilize quasi-2D LiDAR sensors, which offer a compact and scalable solution for AVs. However, point clouds generated by these sensors have a narrow vertical field of view and contain a limited number of points compared to 3D LiDARs, which makes object detection significantly more difficult. To overcome this challenge, we transform the quasi-2D point clouds into Bird's-Eye-View (BEV) images and apply four different input encoding strategies. These encodings enhance the spatial and temporal context of the data, making them suitable for efficient deep-learning-based 2D object detection models to perform real-time detection with minimal computational overhead. In contrast to other approaches for 2D LiDARs, we are able to detect not only vehicles but also pedestrians and cyclists. We use a large real-world dataset to compare our proposed input encodings combined with three different state-of-the-art 2D object detection models. Our results demonstrate that these input encodings enhance object detection performance across all models, significantly reducing false positives, improving heading angle prediction, and boosting the detection accuracy of smaller traffic participants.