Phenomenological Modelling of Camera Performance for Road Marking Detection

Abstract

With the development of autonomous driving technology, the requirements for machine perception have increased significantly. In particular, camera-based lane detection plays an essential role in autonomous vehicle trajectory planning. However, lane detection is subject to high complexity, and it is sensitive to illumination variation, appearance, and age of lane marking. In addition, the sheer infinite number of test cases for highly automated vehicles requires an increasing portion of test and validation to be performed in simulation and X-in-the-loop testing. To model the complexity of camera-based lane detection, physical models are often used, which consider the optical properties of the imager as well as image processing itself. This complexity results in high efforts for the simulation in terms of modelling as well as computational costs. This paper presents a Phenomenological Lane Detection Model (PLDM) to simulate camera performance. The innovation of the approach is the modelling technique using Multi-Layer Perceptron (MLP), which is a class of Neural Network (NN). In order to prepare input data for our neural network model, massive driving tests have been performed on the M86 highway road in Hungary. The model’s inputs include vehicle dynamics signals (such as speed and acceleration, etc.). In addition, the difference between the reference output from the digital-twin map of the highway and camera lane detection results is considered as the target of the NN. The network consists of four hidden layers, and scaled conjugate gradient backpropagation is used for training the network. The results demonstrate that PLDM can sufficiently replicate camera detection performance in the simulation. The modelling approach improves the realism of camera sensor simulation as well as computational effort for X-in-the-loop applications and thereby supports safety validation of camera-based functionality in automated driving, which decreases the energy consumption of vehicles.

Generic Sensor Model Usecase Exemplified by Pedestrian Crossing

“…The article [27] presents a phenomenological lane detection model to simulate camera performance. The model is in the form of a feedforward neural network.…”

Advances in Automated Driving Systems

“…The topic of drowsiness classification in the context of driving automation was investigated in [5]. In simulation of Automated Driving (AD) functions, modelling of camera sensors is often carried out with physical modelling; however, research in [6] presented an alternative with phenomenological modeling. Article [7] introduced a conflicted management framework, especially focusing on aiming at managing urban and peri-urban traffic.…”