Occlusion Model—A Geometric Sensor Modeling Approach for Virtual Testing of ADAS/AD Functions

Abstract

New advanced driver assistance system/automated driving (ADAS/AD) functions have the potential to significantly enhance the safety of vehicle passengers and road users, while also enabling new transportation applications and potentially reducing CO2 emissions. To achieve the next level of driving automation, i.e., SAE Level-3, physical test drives need to be supplemented by simulations in virtual test environments. A major challenge for today's virtual test environments is to provide a realistic representation of the vehicle's perception system (camera, lidar, radar). Therefore, new and improved sensor models are required to perform representative virtual tests that can supplement physical test drives. In this article, we present a computationally efficient, mathematically complete, and geometrically exact generic sensor modeling approach that solves the FOV (field of view) and occlusion task. We also discuss potential extensions, such as bounding-box cropping and sensor-specific, weather-dependent FOV-reduction approaches for camera, lidar, and radar. The performance of the new modeling approach is demonstrated using camera measurements from a test campaign conducted in Hungary in 2020 plus three artificial scenarios (a multi-target scenario with an adjacent truck occluding other road users and two traffic jam situations in which the ego vehicle is either a car or a truck). These scenarios are benchmarked against existing sensor modeling approaches that only exclude objects that are outside the sensor's maximum detection range or angle. The modeling approach presented can be used as is or provide the basis for a more complex sensor model, as it reduces the number of potentially detectable targets and therefore improves the performance of subsequent simulation steps.

Bayesian Gaussian Mixture Models for Enhanced Radar Sensor Modeling: A Data-Driven Approach towards Sensor Simulation for ADAS/AD Development

“…The object lists are typically of very similar structure for camera [7][8][9], lidar, and radar [10], usually containing basic information about the size, position, and speed of the objects. A geometric model for example, focusing on occlusion effects, can be found in [11] and a general approach for object-list models is stated in [12].…”

“…The row defines which objects are occluded by the current object, and the column defines which objects are occluding the current object. For more details, refer to Genser et al's original work on the occlusion model [11]. To illustrate, consider the example scenario below, which shows two vehicles in the Field-of-View of the ego vehicle.…”