When designing the Sim4CAMSens Winter testing campaign, as with any real world test work, one of the key concerns was to minimise the number of variables that could impact the experiments.

LiDAR is a crucial sensor for autonomous vehicles, providing detailed environmental mapping through point clouds. To develop a robust LiDAR model capable of simulating the sensors performance in winter conditions, real-world testing has been carried out.

When developing the winter test programme, a key consideration was to make sure that we could capture data from a number of different sensors so that we can try to learn how the physics of different sensor technologies are affected by weather rather than measure the performance of a few specific devices.

Sensor degradation in poor weather conditions poses a significant challenge to the safety and effectiveness of Autonomous Vehicles (AVs), necessitating urgent research and development.

The Sim4CAMSens project has recently updated the unique CAV Catalogue, now offering a comprehensive global directory of over 150 Level 4 Automated Vehicles (AVs).

We were thrilled to host the Dissemination Workshop for the Innovate UK Sim4CAMSens Project as part of the inaugural event of the IEEE ITSS UK and Ireland Chapter.

During our first winter testing campaign (January to March 2024), we noticed some uncertainties in the results, which we suspected were caused by ice build up on the targets or freezing temperatures affecting the sensor performance. These factors could alter how the targets reflect signals, so we needed to investigate further.