Jonas Bärgman
Jonas Bärgman is a researcher / associate professor, and lead the research group Safety Evaluation in the Division of Vehicle Safety, at the Mechanics and Maritime Department. Jonas research has several components, all aimed at understanding how traffic safety is affected by driver behavior, vehicles (including intelligent systems/vehicle automation), and the environment (e.g., other road-users), in the pre-crash phase. His research ranges from quantifying driver comfort zone boundaries in everyday driving, via the understanding of why crashes occur (crash causation mechanisms) and to develop quantitative models of driver behavior in critical situation, to the development and application of the counterfactual (computer) simulation method to evaluate the safety impact of driver behaviors, driver support and automated systems, and the environment. A majority of the research use different forms of naturalistic driving data as a key component, but also test-track, on-road and simulator experiments are research tools Jonas uses.
Jonas is examiner for the Vehicle and Traffic Safety (TME202) masters course in the masters program for automotive engineering at Chalmers. He also is a lecturer in the Active Safety course in the same program, and active in ISO.
Showing 69 publications
Analysis of Time-to-Lane-Change-Initiation Using Realistic Driving Data
Modeling Lead-Vehicle Kinematics for Rear-End Crash Scenario Generation
Modeling Lead-Vehicle Kinematics for Rear-End Crash Scenario Generation
Continuous Experimentation and Human Factors An Exploratory Study
Managing Human Factors in Automated Vehicle Development: Towards Challenges and Practices
A reference-driver model for overtaking a cyclist
Human factors in developing automated vehicles: A requirements engineering perspective
Continuous Experimentation and Human Factors: An Exploratory Study
Do Car Drivers Respond Earlier to Close Lateral Motion Than to Looming?
Vulnerable road users and the coming wave of automated vehicles: Expert perspectives
Naturalistic Driving Studies: An Overview and International Perspective
Drivers overtaking cyclists in the real-world: evidence from a naturalistic driving study
How safe is tuning a radio?: using the radio tuning task as a benchmark for distracted driving
Great expectations: A predictive processing account of automobile driving
What Are Drivers Doing When They Aren't on the Cell Phone?
Risk factors, crash causation and everyday driving
A quantitative driver model of pre-crash brake onset and control
The UDrive dataset and key analysis results
When, where and how often do professional drivers use their mobile phones?
Interactions with vulnerable road users
Car drivers overtaking cyclists: A European perspective using naturalistic driving data
Using Wireless Communication to Control Road-user Interactions in the Real World
Methods for Analysis of Naturalistic Driving Data in Driver Behavior Research
Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk
Chunking: a procedure to improve naturalistic data analysis
Deliverable D3.3: Data management in euroFOT
On data security and analysis platforms for analysis of naturalistic driving data
Chunking: a Method to Increase Robustness of Naturalistic Field-Operational-Test Data Analysis
An invariant may drive the decision to encroach at unsignalized intersections
Offset Eliminative Map Matching Algorithm for Intersection Active Safety
Sensor Fusion for Vehicle Positioning in Intersection Active Safety Applications
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Showing 15 research projects
Efficient human-centered safety systems (EFFECT)
V4SAFETY Vehicles and VRU Virtual eValuation of Road Safety
Improved quantitative driver behavior models and safety assessment methods for ADAS and AD (QUADRIS)
Cyclist Interaction with Automated Vehicles – CI-AV
Safety in automated driving (ADS): modelling interaction between road-users and automated vehicles
User and safety benefit evaluation on autonomous driving
Quantitative Driver Behaviour Modelling for Active Safety Assessment Expansion (QUADRAE)
Analysis of CRASH Event Data recorder data with video (CRASHED)
Analysis Framework for Safety Systems and Services
Analysis of the SHRP2 Naturalistic Driving Study Data