Marco Dozza
Marco Dozza received the M.E. degree from the University of Bologna (Bologna, Italy) in 2002 and the Ph.D. degree in bioengineering from the University of Bologna, in collaboration with Oregon Health & Science University (Portland, OR, USA) in 2007. After graduation, he worked as a System Developer for over two years with Volvo Technology, a research and innovation company inside the Volvo group. Since 2009, he has been at Chalmers University of Technology (Göteborg, Sweden) where he is a Professor. Marco Dozza is examiner for the course Active Safety in the Master’s Programme for Automotive Engineering. He is also affiliated with the SAFER Vehicle and Traffic Safety Center, where he leads several projects on traffic safety.
Showing 93 publications
Driver conflict response during supervised automation: Do hands on wheel matter?
Automation aftereffects: the influence of automation duration, test track and timings
A computational driver model to predict driver control at unsignalised intersections
How do oncoming traffic and cyclist lane position influence cyclist overtaking by drivers?
Modelling discomfort: How do drivers feel when cyclists cross their path?
How do drivers overtake pedestrians? Evidence from field test and naturalistic driving data
Modelling Interaction between Cyclists and Automobiles - Final Report
Modelling cyclists’ comfort zones from obstacle avoidance manoeuvres
Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic
What is the relation between crashes from crash databases and near crashes from naturalistic data?
Drivers overtaking cyclists in the real-world: evidence from a naturalistic driving study
E-bikers’ braking behavior: Results from a naturalistic cycling 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
How do drivers overtake cyclists?
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?
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
Integrating road safety data for single-bicycle crash causation
Introducing naturalistic cycling data: What factors influence bicyclists' safety in the real world?
Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk
Do cyclists on e-bikes behave differently than cyclists on traditional bicycles?
Platform Enabling Intelligent Safety Applications for Vulnerable Road Users
Understanding Bicycle Dynamics and Cyclist Behavior from Naturalistic Field Data
Driving context and visual-manual phone tasks influence glance behavior in naturalistic driving
Chunking: a procedure to improve naturalistic data analysis
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?
What factors influence drivers' response time for evasive maneuvers in real traffic?
BikeSAFE – Analysis of Safety-Critical Events from Naturalistic Cycling Data
Piloting the Naturalistic Methodology on Bicycles
Recognizing Safety-critical Events from Naturalistic Driving Data
Deliverable D3.3: Data management in euroFOT
Collection of naturalistic bicycling data is now ongoing
Set-up and real-traffic assessment of an active-safety platform for vulnerable-road-users
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
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
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 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 27 research projects
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