Reliability of C-ADAS and the importance of the acceleration function for cycling safety
DOI:
https://doi.org/10.55329/zzsz4880Keywords:
cooperative ADAS (C-ADAS), crash prevention, cross-power spectrum, cycling safety, entropy, traffic conflict analysisAbstract
Driving characteristics of bicyclists and motorists differ significantly in critical, uncritical and unaffected situations in road traffic. When bicyclists cross the path of right-turning motorists, bicyclists seem to mitigate conflicts that can develop into crashes, while motorists seem to avoid non-critical but close interactions that can develop into conflicts. This is one of the key findings of the evaluation of a recently developed and successfully tested cooperative driver assistance system (C-ADAS) that warns right-turning motorists of potential collisions. The warning is given by a special traffic light, which we called ‘amber light’, lighting up only in dangerous situations. Whether a situation becomes dangerous or not is determined by a decision tree, fed by the measured kinematics and specific surrogate measures of safety of the interacting road users. Most notably, the results demonstrate that criticality can be rated by measuring anticipation (or surprise) by computing the cross-power spectrum and applying entropy metric on the acceleration functions of the road users. However, one of the outcomes is that the time for the road users to perceive the amber light state might be too low to react properly. These findings can be used to improve the performance of such a C-ADAS.
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Author Biographies
Marek Junghans, The German Aerospace Center (DLR), Germany
Marek Junghans is a research associate at German Aerospace Center (DLR), Institute of Transportation Systems. He received his Docorate (Dr.-Ing.) from Dresden University of Technology in Intelligent Transportation Systems. His research interests cover stochastic signal processing and traffic safety with strong focus on cycling safety, measuring and understanding traffic behaviour to improve safety.
CRediT contribution: Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Software, Supervision, Visualization, Writing—original draft.
Meng Zhang, The German Aerospace Center (DLR), Germany
Meng Zhang is a research associate at the Institute of Transportation Systems of German Aerospace Center (DLR). He received his Doctorate (Dr. rer. nat.) from Technical University of Braunschweig in 2023 and completed his Master of Science (M.Sc.) in Human Factors at the Technical University of Berlin in 2016. His research primarily focuses on the assessment of emotions of road users, and the modelling of interaction and cooperation between road users.
CRedit contribution: Data curation, Resources, Software, Validation, Writing—review & editing.
Hagen Saul, The German Aerospace Center (DLR), Germany
Hagen Saul is a research associate at the Institute of Transportation System of German Aerospace Center (DLR) and currently Ph.D. candidate at the University of Wuppertal, Germany. He received his Diploma in Computer Science (Dipl.-Inf.) from University of Koblenz-Landau in 2011. His research interests include traffic conflicts, behavioural patterns, time series analysis, machine learning in general, trajectory and risk prediction.
CRediT contribution: Conceptualization, Data curation, Resources, Software, Validation, Writing—review & editing.
Andreas Leich, German Aerospace Center (DLR)
Andreas Leich is a research associate at German Aerospace Center (DLR), Institute of Transportation Systems. He received his Doctorate (Dr.-Ing.) from Dresden University of Technology and worked for several years as a development engineer in the German automotive industry. His research interests cover sensor data processing for traffic safety research.
CRediT contribution: Conceptualization, Writing—review & editing.
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Copyright (c) 2024 Marek Junghans, Meng Zhang, Hagen Saul, Andreas Leich
This work is licensed under a Creative Commons Attribution 4.0 International License.
