9 papers
Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review
Jörg Gamerdinger, Sven Teufel, Oliver Bringmann
Accurate lane detection is essential for automated driving, enabling safe and reliable vehicle navigation across a variety of road scenarios. Numerous datasets have been introduced…
CarlaNCAP: A Framework for Quantifying the Safety of Vulnerable Road Users in Infrastructure-Assisted Collective Perception Using EuroNCAP Scenarios
Jörg Gamerdinger, Sven Teufel, Simon Roller +1
The growing number of road users has significantly increased the risk of accidents in recent years. Vulnerable Road Users (VRUs) are particularly at risk, especially in urban envir…
EPSM: A Novel Metric to Evaluate the Safety of Environmental Perception in Autonomous Driving
Jörg Gamerdinger, Sven Teufel, Stephan Amann +2
Extensive evaluation of perception systems is crucial for ensuring the safety of intelligent vehicles in complex driving scenarios. Conventional performance metrics such as precisi…
Criticality Metrics for Relevance Classification in Safety Evaluation of Object Detection in Automated Driving
Jörg Gamerdinger, Sven Teufel, Stephan Amann +1
Ensuring safety is the primary objective of automated driving, which necessitates a comprehensive and accurate perception of the environment. While numerous performance evaluation…
CoLD Fusion: A Real-time Capable Spline-based Fusion Algorithm for Collective Lane Detection
Jörg Gamerdinger, Sven Teufel, Georg Volk +1
Comprehensive environment perception is essential for autonomous vehicles to operate safely. It is crucial to detect both dynamic road users and static objects like traffic signs o…
LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection Systems in Autonomous Driving
Jörg Gamerdinger, Sven Teufel, Stephan Amann +2
Comprehensive perception of the vehicle's environment and correct interpretation of the environment are crucial for the safe operation of autonomous vehicles. The perception of sur…