Sim4CAMSens

SPRING

Sim4CAMSens

Modelling, Simulation and Testing of Automotive Perception Sensors

Sim4CAMSens is a series of InnovateUK (CCAV) funded projects developing methods to quantify and model camera, LiDAR, and radar sensor performance under all conditions for ADAS, Automated Vehicles, and In-cabin simulations.
The final report of the Sim4CAMSens1 can be found here.

Report on Noise Factor Analysis

The SPRING group led the delivery of the D2.1. Report on Noise Factor Analysis_v2025
This report presents a literature review of current testing methods and standards for automotive perception sensors, including cameras, LiDAR, and radar. It also examines testing frameworks for sensorfusion suites, objectdetection systems, and Euro NCAP protocols. Furthermore, this report summarises the insights from the Sim4CAMSens Noise Factors Workshop, highlighting the most significant noise factors affecting these sensor technologies. Finally, the report presents an extensive qualitative and quantitative analysis of sensor data gathered during the Winter Testing Campaigns. This analysis demonstrates how various environmental noise sources can impact sensor data quality (using image and pointcloud quality metrics) and perception performance (using a trafficsignrecognition algorithm).

LiDAR Noise Factor Testing in the Lab

The SPRING group led the delivery of the D2.2. Test Methodology Report
This report systematically investigates how environmental noise factors – specifically LiDAR occlusion caused by clear and muddy water droplets – affect sensor performance. Through controlled laboratory experiments, we applied droplets directly to the active surfaces of various stateoftheart automotive LiDAR units. By comparing the resulting pointcloud data, we were able to quantify the degradation in LiDAR data quality under recreated adverse conditions. These findings deepen our understanding of sensorocclusion phenomena and their potential implications for vehicle safety.