Marco Dozza
Marco Dozza is a Full 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 group 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 group 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 (www.dozza.eu). 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. Marco also teaches the course Micromobility for a sustainable future within TRACKS.
Showing 126 publications
Naturalistic micromobility data: opportunities and threats
Driver Visual Attention Before and After Take-Over Requests During Automated Driving on Public Roads
How do drivers interact with cyclists at unsignalized intersections? A driving simulator study
Micromobility: new road-user interactions in the urban landscape
The right turn: Investigating interactions between drivers and e-scooter riders
Seeing is Believing: How Artificial Eyes Are Making Micromobility Safer
Drivers passing cyclists: How does sight distance affect safety? Results from a naturalistic study
Modelling Braking and Steering Avoidance Maneuvers for Micromobility
E-Scooters: Transport or leisure? Findings from naturalistic data collection
Modeling collision avoidance maneuvers for micromobility vehicles
Driver response to take-over requests in real traffic
Drivers’ and cyclists’ safety perceptions in overtaking maneuvers
Modeling the Braking Behavior of Micro-Mobility Vehicles
Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic
Data Augmentation via Neural-Style-Transfer for Driver Distraction Recognition
It’s about time! Earlier take-over requests in automated driving enable safer responses to conflicts
Automation aftereffects: the influence of automation duration, test track and timings
Driver conflict response during supervised automation: Do hands on wheel matter?
The development of cycling in european countries since 1990
How do drivers overtake pedestrians? Evidence from field test and naturalistic driving data
How do oncoming traffic and cyclist lane position influence cyclist overtaking by drivers?
Modelling cyclists’ comfort zones from obstacle avoidance manoeuvres
What is the relation between crashes from crash databases and near crashes from naturalistic data?
Modelling discomfort: How do drivers feel when cyclists cross their path?
A computational driver model to predict driver control at unsignalised intersections
Modelling Interaction between Cyclists and Automobiles - Final Report
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
Safety Science Special Issue on Cycling Safety
Crash Risk: How Cycling Flow Can Help Explain Crash Data
An Open-Source Data Logger for Field Cycling Collection: Design and Evaluation
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
What is the relation between crashes from crash databases and near-crashes from naturalistic data?
Evaluation of a new narrow and tiltable electric tricycle (e-trike) concept
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
Do cyclists on e-bikes behave differently than cyclists on traditional bicycles?
Integrating road safety data for single-bicycle crash causation
Platform Enabling Intelligent Safety Applications for Vulnerable Road Users
Understanding Bicycle Dynamics and Cyclist Behavior from Naturalistic Field Data
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
Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk
Chunking: a procedure to improve naturalistic data analysis
What factors influence drivers' response time for evasive maneuvers in real traffic?
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?
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
An Open Customizable Modular Platform For Analysis of Human Movement in Laboratory and Outdoors
What is the most effective type of audio-biofeedback for postural motor learning?
euroFOT: constrains and trade-offs in testing hypotheses
Chunking: a Method to Increase Robustness of Naturalistic Field-Operational-Test Data Analysis
SAFER100Car: a toolkit to analyze data from the 100 Car Naturalistic Driving Study
Vibrotactile biofeedback improves tandem gait in patients with unilateral vestibular loss.
Auditory biofeedback substitutes for loss of sensory information in maintaining stance.
Audio-biofeedback improves balance in patients with bilateral vestibular loss.
Audio-biofeedback for balance improvement: an accelerometry-based system.
Influence of a portable audio-biofeedback device on structural properties of postural sway.
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Showing 34 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)
Modelling Interaction between Cyclists and Automobiles 2
Modellering av Interaktion mellan Cyklister och Fordon 2- MICA2
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