The ROADVIEW consortium introduced Operational Design Domain (ODD) definitions for five different use cases, particularly detailing traffic density, drivable areas and harsh weather conditions, with a specific focus on rain, fog, and snow. These ODD definitions were specified by extending the ODD taxonomy defined in ISO 34503 to include the ROADVIEW use cases and relevant environmental conditions for different modalities, such as RGB cameras, LiDAR, RADAR, and thermal cameras. To design and validate autonomous systems in adverse weather, ROADVIEW introduced the following items: a) generation of high-fidelity digital twins of some of the testing/demo facilities; b) creation of harsh weather paired dataset including data generated by most of the automotive perception sensors modalities (i.e. LiDAR, RADAR, thermal and colour cameras) and c) validated sensor noise models for the above-mentioned sensors, using the collected data and novel metrics to evaluate the performance of sensor models in the context of assisted and automated driving.
ROADVIEW’s data processing performance was improved by using the concept of Data Readiness Level (DRL) to quantitatively show where the images from RGB and thermal cameras are unsharp, distorted, blurred, over/under exposed or where noise is present in the LiDAR and RADAR point clouds. We started with 43 image quality tools and five different point cloud quality tools.
ROADVIEW developed an enhanced in-vehicle perception system that integrates multiple sensor modalities to enable robust operation under harsh weather conditions and in a wide range of traffic scenarios. The perception system fuses a set of LiDAR, RADAR, and colour cameras to build an integrated world view around the vehicle in various traffic and weather conditions. The selection of sensors is based on the best findings from existing setups of multiple partners (AVL, FGI, FORD, THI, VTT, and S4) developing their own autonomous driving platforms within the consortium. The ROADVIEW perception stack includes also the methods and algorithms for sensor fusion, object detection, free space detection and weather type detection.
Furthermore, the perception system includes in-vehicle visibility and slipperiness estimators which utilize the vehicle's own sensors to estimate the visibility conditions around the vehicle, and the grip on the road surface in front of the vehicle to enable robust operation in harsh weather conditions. The positioning capabilities of the vehicle in difficult weather are improved using the novel environment-aware normal distribution transform (EA-NDT) based HD-mapping method.
ROADVIEW developed a weather-aware decision-making system that incorporates the state estimates in the decision-making process to adjust the vehicle behaviour. The consortium developed the decision-making system, which includes the weather-aware navigation system and velocity controller as well as Infrastructure-based manoeuvre cooperation software by following the reference architecture for the control and decision-making system. The software implementations of these systems were successfully tested and evaluated following the test scenarios. Evaluation was conducted with simulations and real-world tests in winter conditions.
ROADVIEW integrated advanced XiL (X-in-the-Loop) test methods, incorporating fine-tuned camera and LiDAR sensor models as well as high-fidelity vehicle dynamics models. Noise models for adverse weather conditions were also implemented in simulation environments. Supported by high-precision digital twins and stimulation technologies based on Over-the-Air (OTA) and Direct Data Injection (DDI) interfaces, both Hardware-in-the-Loop (HiL) and Vehicle-in-the-Loop (ViL) test setups were successfully integrated and are now operational. These stimulation methods enable ROADVIEW partners to interface simulation environments with vehicle hardware in the same manner as with real sensors, allowing for a seamless transition from XiL-based testing to real-world validation.
