Best Practices in Sensor Selection for Object Detection in Autonomous Driving: A Practitioner’s Perspective

<div class="section abstract"><div class="htmlview paragraph">Object detection (OD) is one of the most important aspects in Autonomous Driving (AD) application. This depends on the strategic sensor’s selection and placement of sensors around the vehicle. The sensors should be selected based on various constraints such as range, use-case, and cost limitation. This paper introduces a systematic approach for identifying the optimal practices for selecting sensors in AD object detection, offering guidance for those looking to expand their expertise in this field and select the most suitable sensors accordingly. In general, object detection typically involves utilizing RADAR, LiDAR, and cameras. RADAR excels in accurately measuring longitudinal distances over both long and short ranges, but its accuracy in lateral distances is limited. LiDAR is known for its ability to provide accurate range data, but it struggles to identify objects in various weather conditions. On the other hand, camera-based systems offer superior recognition capabilities but lack the precision in range resolution. Fusion of all the three sensors could improve object detection results, however at a higher cost and may be redundant in some cases. In autonomous driving, different functions like dynamic fusion, static fusion, and road model are used to detect a variety of objects like vehicles, motorcycles, guardrails, and road lanes. The paper presents an in-depth analysis of the mechanisms of each sensor, the nature of the data it generates, its level of accuracy, and the limitations it encounters in detecting various objects. For each object, the paper outlines important steps and recommendations that can be implemented to achieve optimal results. This paper elucidates a framework for multi-sensor fusion in object detection, demonstrating superior performance through a practical use case. Model output is rigorously validated against ground truth data using proven devices. The proposed methodology yields demonstrably improved and refined obstacle and environment classification.</div></div>