Marco Dozza
Marco Dozza is a Professor in Active Safety and Road-user Behavior in the Department of Mechanics and Maritime Sciences at Chalmers University of Technology. Within this department, he leads the Unit on Crash Analysis and Prevention (CAP) in the Vehicle Safety Division. Within CAP, Marco's research activities include modelling road-user behaviour, development and evaluation of active safety, human interaction with automation, and cycling safety. The research of the CAP unit is rooted in cognitive science and neuroscience, combines engineering and human factors, and relays on naturalistic and experimental data.
Marco earned his Ph.D. in Bioengineering from University of Bologna, Italy in collaboration with Oregon Health and Science University, Portland OR, USA (). After graduation, he worked as System Developer for over 2 years at Volvo Technology, a research and innovation company inside the Volvo group. Since 2009, he has been an Examiner for the course Active Safety in the Master’s Programme for Mobility Engineering.
Showing 118 publications
Driver Visual Attention Before and After Take-Over Requests During Automated Driving on Public Roads
Modelling Braking and Steering Avoidance Maneuvers for Micromobility
Drivers passing cyclists: How does sight distance affect safety? Results from a naturalistic study
E-Scooters: Transport or leisure? Findings from naturalistic data collection
Driver response to take-over requests in real traffic
Modeling collision avoidance maneuvers for micromobility vehicles
Data Augmentation via Neural-Style-Transfer for Driver Distraction Recognition
Modeling the Braking Behavior of Micro-Mobility Vehicles
Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic
Drivers’ and cyclists’ safety perceptions in overtaking maneuvers
Automation aftereffects: the influence of automation duration, test track and timings
It’s about time! Earlier take-over requests in automated driving enable safer responses to conflicts
Driver conflict response during supervised automation: Do hands on wheel matter?
The development of cycling in european countries since 1990
What is the relation between crashes from crash databases and near crashes from naturalistic data?
How do oncoming traffic and cyclist lane position influence cyclist overtaking by drivers?
How do drivers overtake pedestrians? Evidence from field test and naturalistic driving data
Modelling cyclists’ comfort zones from obstacle avoidance manoeuvres
A computational driver model to predict driver control at unsignalised intersections
Modelling Interaction between Cyclists and Automobiles - Final Report
Modelling discomfort: How do drivers feel when cyclists cross their path?
E-bikers’ braking behavior: Results from a naturalistic cycling study
Drivers overtaking cyclists in the real-world: evidence from a naturalistic driving study
A new framework for modelling road-user interaction and evaluating active safety systems
An Open-Source Data Logger for Field Cycling Collection: Design and Evaluation
Safety Science Special Issue on Cycling Safety
Crash Risk: How Cycling Flow Can Help Explain Crash Data
Car drivers overtaking cyclists: A European perspective using naturalistic driving data
Using Wireless Communication to Control Road-user Interactions in the Real World
Using naturalistic data to assess e-cyclist behavior
Evaluation of a new narrow and tiltable electric tricycle (e-trike) concept
What is the relation between crashes from crash databases and near-crashes from naturalistic data?
How do drivers overtake cyclists?
Real-world effects of using a phone while driving on lateral and longitudinal control of vehicles
On the Potential of Accelerating an Electrified Lead Vehicle to Mitigate Rear-End Collisions
Platform Enabling Intelligent Safety Applications for Vulnerable Road Users
Integrating road safety data for single-bicycle crash causation
Do cyclists on e-bikes behave differently than cyclists on traditional bicycles?
Understanding Bicycle Dynamics and Cyclist Behavior from Naturalistic Field Data
Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk
Introducing naturalistic cycling data: What factors influence bicyclists' safety in the real world?
Driving context and visual-manual phone tasks influence glance behavior in naturalistic driving
BikeSAFE – Analysis of Safety-Critical Events from Naturalistic Cycling Data
BikeCOM – A cooperative safety application supporting cyclists and drivers at intersections
What is the Relation between Bicycle Dynamics and Safety in the Real World?
What factors influence drivers' response time for evasive maneuvers in real traffic?
Chunking: a procedure to improve naturalistic data analysis
Dialling, texting, and reading in real world driving: When do drivers choose to use mobile phones?
Collection of naturalistic bicycling data is now ongoing
Set-up and real-traffic assessment of an active-safety platform for vulnerable-road-users
Piloting the Naturalistic Methodology on Bicycles
Recognizing Safety-critical Events from Naturalistic Driving Data
Deliverable D3.3: Data management in euroFOT
Timing Matters: Visual behaviour and crash risk in the 100‐car on‐line data
On data security and analysis platforms for analysis of naturalistic driving data
What is the most effective type of audio-biofeedback for postural motor learning?
An Open Customizable Modular Platform For Analysis of Human Movement in Laboratory and Outdoors
SAFER100Car: a toolkit to analyze data from the 100 Car Naturalistic Driving Study
euroFOT: constrains and trade-offs in testing hypotheses
Chunking: a Method to Increase Robustness of Naturalistic Field-Operational-Test Data Analysis
Vibrotactile biofeedback improves tandem gait in patients with unilateral vestibular loss.
Auditory biofeedback substitutes for loss of sensory information in maintaining stance.
Audio-biofeedback for balance improvement: an accelerometry-based system.
Audio-biofeedback improves balance in patients with bilateral vestibular loss.
Influence of a portable audio-biofeedback device on structural properties of postural sway.
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Showing 33 research projects
MicroITS - Computational models for a safe integration of micromobility in the transport system
Safe integration of micro-mobility in the transport system - SIMT
e-SAFER - Computational models for a safe interaction between (automated) vehicles and e-scooters
Cyclist Interaction with Automated Vehicles – CI-AV
AI for Analysis for Naturalistic Driving Data
MULTI-CUE: A comparative study of motion, verbal and visual cues in automated driving systems (ADS)
Modellering av Interaktion mellan Cyklister och Fordon 2- MICA2
Modelling Interaction between Cyclists and Automobiles 2
FOT-E (Field Operational Test dataset Enrichment)
Characterizing and classifying new e-vehicles for personal mobility
Driver models for automated driving
MICA - Modelling Interaction between Cyclists and Automobiles
Safety in automated driving (ADS): modelling interaction between road-users and automated vehicles
L3Pilot - Piloting Automated Driving on European Roads
Cyklistkomfortgränser: forskningsöversikt och experimentell ram
Measures for behaving safely in traffic (MeBeSafe)
Quantitative Driver Behaviour Modelling for Active Safety Assessment Expansion (QUADRAE)
DIV - Driver Interaction with Vulnerable Road Users
BikeModel: Modeller för cyklistbeteende
Human Factors of Automated Driving (HFAUTO)
Analysis of the SHRP2 Naturalistic Driving Study Data