Systematic Analysis of the Sensor Coverage of Automated Vehicles Using Phenomenological Sensor Models

TL;DR

This paper systematically analyzes sensor coverage in automated vehicles using phenomenological models to identify relevant scenarios, balancing model reliability and computational efficiency for safety assessments.

Contribution

It introduces phenomenological sensor models for camera, ultrasonic, radar, and lidar, enabling systematic scenario analysis with higher reliability and lower computational costs.

Findings

Significant differences in system configurations affect sensor coverage.

Phenomenological models outperform ideal models in reliability.

Models enable targeted scenario selection for safety assessment.

Abstract

The objective of this paper is to propose a systematic analysis of the sensor coverage of automated vehicles. Due to an unlimited number of possible traffic situations, a selection of scenarios to be tested must be applied in the safety assessment of automated vehicles. This paper describes how phenomenological sensor models can be used to identify system-specific relevant scenarios. In automated driving, the following sensors are predominantly used: camera, ultrasonic, \radar and \lidarohne. Based on the literature, phenomenological models have been developed for the four sensor types, which take into account phenomena such as environmental influences, sensor properties and the type of object to be detected. These phenomenological models have a significantly higher reliability than simple ideal sensor models and require lower computing costs than realistic physical sensor models, which represents an optimal compromise for systematic investigations of sensor coverage. The simulations showed significant differences between different system configurations and thus support the system-specific selection of relevant scenarios for the safety assessment of automated vehicles.