Prediction-Uncertainty-Aware Threat Detection for ADAS: A Case Study on Lane-Keeping Assistance
Journal article, 2023

Advanced driver assistance systems typically support the driver in cases where the driver is likely to fail the driving task. The challenge, from a system perspective, is to accurately detect those cases. Recently, machine learning-based prediction models that are able to estimate the prediction uncertainty in real-time have successfully been introduced for this purpose. However, very little effort has been made on using the prediction uncertainty in the decision-making logic to improve the system's robustness, especially in cases where the input data is affected by noise or anomalies that are not presented in the training data. In this work, four threat-detection methods using uncertainty estimates are proposed and evaluated using a real-world data set. The methods use different strategies for leveraging uncertainty information, where the goal is to ensure that the intervention decision is based on trustworthy predictions. The threat-detection methods' performances are evaluated, using five different learning-based prediction models, in the context of a lane-keeping assistance application.

Computational modeling


machine learning

lane-keeping assistance

threat detection




Decision making

prediction uncertainty

Predictive models

Real-time systems


John Dahl

Chalmers, Electrical Engineering, Systems and control

Gabriel Rodrigues de Campos

Research and Development

Jonas Fredriksson

Chalmers, Electrical Engineering, Systems and control

IEEE Transactions on Intelligent Vehicles

23798858 (eISSN)

Vol. 8 4 2914-2925

Subject Categories

Computer Science

Computer Systems



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