# Evaluation and Testing System for Automotive LiDAR Sensors
## 1 Introduction  
The world is undergoing an unprecedented technological transformation in which vehicles and autonomous driving systems are evolving at a breathtaking pace [1; 2; 3; 4]. Optimistic predictions claim that by 2030 autonomous vehicles will be sufficiently reliable, affordable, and common to displace most human driving, providing huge savings and benefits [5]. However, most of the vehicles today are manually controlled, and in order to achieve full driving autonomy they must evolve through different levels of driving automation, as defined by the American Society of Automotive Engineers (SAE) [6]; levels 0--No Driving Automation, 1--Driver Assistance, and 2--Partial Driving Automation require a human driver to monitor the driving environment, while in levels 3--Conditional Automation, 4--High Automation, and 5--Full Automation the automated system is able to autonomously monitor and navigate the driving environment. Current Level 2 vehicles are provided with advanced driver-assistance systems (ADAS) to help the driver in several situations, such as assisting in parking tasks, providing traffic alerts, promoting collision avoidance with other vehicles and objects, and performing automated decisions in situations that may compromise the safety of all occupants. Nonetheless, in order to cope with these revolutionary trends new solutions at the sensor level must be created to provide vehicles with the ability to hear and see the surrounding environment. An autonomous vehicle requires reliable sensors in order to recreate an accurate mapping of the surroundings, which is only possible with multi-sensor perception systems relying on a combination of radars, cameras, and light detection and ranging (LiDAR) sensors [7; 8; 9; 10], as illustrated in Figure 1. Radar sensors can provide (1) cross-traffic alerts and Blind SpotAssist features, as well as assisting with (2) Adaptive Cruise Control systems; on the other hand, LiDAR sensors can be used to (3) translate the surroundings into a 3D representation, achieving several distances with high levels of accuracy and precision; finally, cameras can help with such features as (4) object detection and classification and (5) collision avoidance. LiDAR sensors are emerging as a mandatory state-of-the-art technology that must be part of any perception system, as they enable a true 3D visualization of the surroundings through a point cloud representation in real time [11; 12; 13; 14]. Accurate and precise measurement of the surroundings with LiDAR sensors can assist the perception systems in several tasks [9], e.g., obstacles, objects, and vehicles detection [15; 16; 17]; pedestrians recognition and tracking [18; 19]; and ground segmentation for road filtering [20], among others [21]. Continual advances around LiDAR are improving its measuring and imaging architectures [12; 22; 23]. Nonetheless, the measurements and the 3D point cloud of a LiDAR sensor can always be corrupted by several noise sources, e.g., internal components [24], mutual interference [25; 26], reflectivity issues [27], light [11], adverse weather conditions [10; 28; 29; 30], and others [31], making it compulsory to test and analyze all sensors' characteristics before they are placed on the market or assembled in a car. The first steps towards creating controlled environments for testing and evaluation of LiDAR sensors have already been taken. For instance, LIBRE [32], the first benchmarking and reference LiDAR dataset, tested ten sensors in three different environments and configurations: (1) static targets, where objects were placed at known distances and measured from a fixed position within a controlled environment; (2) adverse weather, where static obstacles were measured from a moving vehicle (captured in a weather chamber where LiDARs were exposed to different adverse conditions, such as fog, rain, and strong light); and (3) dynamic traffic, where dynamic objects were captured from a vehicle driven on public urban roads at multiple times and at different times of the day.