When driving becomes risky: Micro-scale variants of the lane-changing maneuver in highway traffic

Abstract

Objective: Vehicular lane-changing is one of the riskiest driving maneuvers. Since vehicular automation is quickly becoming a reality, it is crucial to be able to identify when such a maneuver can turn into a risky situation. Recently, it has been shown that a qualitative approach: the Point Descriptor Precedence (PDP) representation, is able to do so. Therefore, this study aims to investigate whether the PDP representation can detect hazardous micro movements during lane-changing maneuvers in a situation of structural congestion in the morning and/or evening.Method: The approach involves analyzing a large real-world traffic dataset using the PDP representation and adding safety distance points to distinguish subtle movement patterns.Results: Based on these subtleties, we label four out of seven and five out of nine lane-change maneuvers as risky during the selected peak and the off-peak traffic hours respectively.Conclusions: The results show that the approach can identify risky movement patterns in traffic. The PDP representation can be used to check whether certain adjustments (e.g., changing the maximum speed) have a significant impact on the number of dangerous behaviors, which is important for improving road safety. This approach has practical applications in penalizing traffic violations, improving traffic flow, and providing valuable information for policymakers and transport experts. It can also be used to train autonomous vehicles in risky driving situations.

Keywords:

• Pattern recognition
• road safety
• spatiotemporal modeling
• vehicle interactions
• data mining
• intelligent transportation system

Acknowledgements

The authors would like to acknowledge “Het Verkeerscentrum, Flanders Belgium” for providing the traffic surveillance video of the selected E411-A4 highway location at Jezus-Eik, Belgium.

Disclosure statement

The authors declare that they are not aware of any competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The datasets and the code supporting the findings of this study are available at https://doi.org/10.6084/m9.figshare.17033315.

Additional information

Funding

This work was supported by the Higher Education Commission (HEC), Pakistan under Grant number 50040696.