Follow Me: Exploring Strategies and Challenges for Collaborative Driving
Authors
Current research on Vehicle-to-Vehicle (V2V) communication aims at improving interaction between different vehicles by communication technologies and is mainly focused on driver-to-driver interaction. But how do drivers and passengers of two vehicles that have the same destination communicate with each other? In such a collaborative driving scenario, several factors such as the environmental context or the behavior of the vehicle occupants may influence the communication. In order to explore how information is exchanged in collaborative driving, we conducted an exploratory in-situ study with seven groups of two driver/co-driver pairs each, located in two separate vehicles. During the ride, the participants had to drive collaboratively on a predefined route solving different subtasks. We found that different social (e.g., driving habits, unpredicted intentions) and contextual factors (e.g., night/rain conditions, size or color of the vehicle) influenced collaboration. Our findings provide a deeper understanding of collaborative driving and inform future V2V communication designs.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 100%
Seeing Beyond the Leading Vehicle: Designing V2V Braking Visualizations to Support Novice Drivers
AutoUI '25· V2X (Vehicle-to-Everything) Communication Design
- 75%
Express What I Think: The Impact of External Human-Machine Interfaces on the Performance of Lane Change Maneuvers
AutoUI '25· External HMI (eHMI) — Communication with Pedestrians & Cyclists +1
- 60%
Portobello: Extending Driving Simulation from the Lab to the Road
CHI '24· Automated Driving Interface & Takeover Design +2
- 60%
Inter-regional Lens on the Privacy Preferences of Drivers for ITS and Future VANETs
CHI '24· V2X (Vehicle-to-Everything) Communication Design +2
- 60%
To Cooperate or Not to Cooperate: A Systematic Review and Meta-Analysis of Human Driving Behavior in Interactions with Autonomous Vehicles
CHI '26· Automated Driving Interface & Takeover Design +2
- 60%
Towards Instrumented Fingerprinting of Urban Traffic: A Novel Methodology using Distributed Mobile Point-of-View Cameras
AutoUI '24· Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS) +1
- 60%
Enhancing Passenger Trust Toward Cooperative Autonomous Vehicles Using Simulated Augmented Reality Displays
AutoUI '25· Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS) +2
Based on Jaccard similarity of research subtopics & professions (≥60%)