Driving Under the Influence: How Music Listening Affects Driving Behaviors
March 27th, 2019
Here, we present a protocol to assess driving behaviors while following a vehicle in a simulated driving environment. The presented protocol is used to compare the impact of different auditory backgrounds on driving behaviors.
This protocol offers a well controlled car driving situation, where participants have to continuously adjust their vehicle speed according to lead vehicle speed. Driving in a simulator is different from driving in real life. All the events inside the simulation have been planned and are under control.
The main advantage of the driving simulation is to provide the same driving condition to different participants, and to be sensitive to inter-individual differences and individual variation from one completion of the protocol to the other. The driving situations have to be carefully thought out well in advance. They also need to be tested by a few pilot participants.
The analyses also need to be computed beforehand, which indicators are we gonna compute, and how do we perform the computation. Demonstrating the procedure will be Vivien Gaujoux, a research engineer in my laboratory. Begin by escorting the participant into the experimental room.
Ensure that they have a driving license, at least years of driving experience, normal or corrected to normal vision and audition. Have the participant provide a USB stick with pre-selected preferred music tracks to create different musical backgrounds during the experiment. Then equip the participant with a heart rate belt linked to a watch that monitors heart rate.
Next, seat the participant in front of the simulator in a modified car seat, approximately 60 centimeters from the screens. Allow the participant to adjust the distance between the seat and the pedals with the handle underneath the seat. Once the participant is comfortably positioned, provide instructions on using the features of the simulator.
Then inform the participant about simulation sickness that can sometimes occur, and let the participant know that the simulation will be stopped at any time if necessary. On the computer screen, show rural roadway with one traffic lane per direction with five left-bends and five right-bends, and without any traffic. Set the simulation driving noise at 25 decibels.
Next, modify the tempo of the music without any pitch modification. Create four auditory backgrounds for the participant:no music, music, music plus 10, and music minus 10. Next, set the intensity of the music to 75 decibels for all auditory backgrounds, except for the no music condition.
Finally, use the software to play one of the four musical backgrounds on two lateral powered monitor speakers located on the right and left of the participant for the entire duration of each experimental drive. Start by providing instructions about the experimental task to the participant. Inform them to drive as if in a real-life situation, and that there is no traffic or obstacles on the path.
Then start the simulation with the training phase in order to familiarize the participant with the driving simulator, the simulated environment, the vehicle controls, and the car-following task. When the participant feels comfortable, stop the training phase. Next, launch the simulated car-following task and one of the four musical backgrounds.
For the simulated car driving task, have the driver first drive for 50 meters before stopping at a stop sign. Once the participant's vehicle has stopped, a leading vehicle appears on the road at the left of the intersection. Instruct the participant to follow the vehicle.
After an initial phase in which the speed of the leading vehicle is stationary and set at 20 kilometers per hour, allow the driven vehicle to catch up. Watch the participant keep driving as the speed then varies sinusoidally between 45 kilometers per hour and 70 kilometers per hour within each period of 60 seconds, for a total of three minutes. Then ask the participant to stop driving.
For the car-following task, use a two-lane road with opposite traffic directions. In order to provide a realistic driving environment, use a road section with a balanced number of left and right bends, with various radii of curvature from 45 meters to 300 meters. Additionally, add visual elements on the edges of the road section, such as trees, barriers, fields, and landform.
Record heart rate data at the beginning of each simulated car-following task, and end it at the end of that driving task. Lastly, allow the participant to take a five-minute break between each car-following task to reduce carryover effects. After the experiment, collect the participant's subjective mood using the Brief Mood Introspection Scale.
Next, compute the mean heart rate and heart rate variability over the entire drive for each experimental condition using the data recorded by the heart rate monitoring watch at a sample per second. Then measure objective driving behaviors through the mean inter-vehicular time and the inter-vehicular time variability. Record both driven and leading vehicle position, and speed at each time step, at a sample rate of 60 hertz.
At each time step, compute the inter-vehicular time as the time required for the driven vehicle to reach the position of the lead vehicle, if the position of the lead vehicle was frozen, and the speed of the driven vehicle was constant. Finally, average all the values collected for a drive to obtain the mean inter-vehicular time, and compute the standard deviation on those values to obtain the inter-vehicular time variability. Results indicated that the music condition had a significant effect on subjective mood as observed on three of the four dimensions of the Brief Mood Introspection Scale when compared with the no music condition.
Considering all four auditory backgrounds, a significant effect on subjective mood was observed on the four dimensions of the BMIS. The mean heart rate was significantly different for the no music and music backgrounds, and was significantly different under the four auditory background conditions as well. The heart rate variability was not significantly impacted by the background condition in either case.
Further, the mean inter-vehicular time was significantly different for the no music and music backgrounds, but was not significantly different under the four auditory background conditions. The standard deviation of the inter-vehicular time was not significantly impacted by the background condition in either case. The instructions delivered to participants are crucial for the success of the protocol.
The experimenter must ensure that participants understand that they should follow the lead vehicle as they would do in real life. The procedure presented does not cover the entire range of possible driving situations. The car-following task can be complemented by the investigation of other driving situations, such as driving on a highway, or in town, for instance.
The main advantage of the driving simulation is the safety offered to the participant compared to real-life driving. However, since some participants may experience motion sickness, we recommend stopping the simulation as soon as the participant feels dizzy.
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Overview
This protocol offers a controlled car driving situation in a simulated environment, allowing participants to adjust their vehicle speed according to a lead vehicle. The simulation ensures consistent driving conditions across different participants, highlighting individual variations in driving behavior.
Key Study Components
Area of Science
- Neuroscience
- Behavioral Science
- Driving Simulation
Background
- Driving in a simulator differs from real-life driving.
- Events in the simulation are planned and controlled.
- The simulation allows for sensitivity to inter-individual differences.
- Careful planning and pilot testing are essential for driving situations.
Purpose of Study
- To assess driving behaviors in a simulated environment.
- To compare the impact of different auditory backgrounds on driving.
- To provide a standardized driving condition for participants.
Methods Used
- Participants adjust vehicle speed based on a lead vehicle.
- Controlled driving simulation environment.
- Comparison of driving behaviors under varying auditory conditions.
- Pilot testing to refine driving scenarios.
Main Results
- Driving behaviors can be effectively assessed in a simulator.
- Auditory backgrounds influence driving performance.
- Individual differences in driving behavior are observable.
- Standardized conditions enhance the reliability of results.
Conclusions
- The protocol is effective for studying driving behaviors.
- Simulated environments can reveal important behavioral insights.
- Future studies can build on this protocol to explore further variables.
Frequently Asked Questions
Executive Industry Relevance
Understanding how environmental stimuli such as music influence driving behaviors provides mechanistic insights into human performance under distraction, relevant for assessing cognitive load in operational settings. This protocol enables standardized evaluation of behavioral responses in a controlled simulation, supporting preclinical-like assessment of neurobehavioral modifiers. The approach aids in de-risking target validation by quantifying how sensory inputs affect motor output and attentional control in a reproducible paradigm.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of how auditory stimuli modulate mood and arousal, informing mechanistic links between sensory input and behavioral output.
- Operational Value: Provides a reproducible platform to assess behavioral changes across conditions, supporting hypothesis-driven target engagement studies.
Screening & Assay Development
- Scientific Value: Generates quantitative behavioral readouts (inter-vehicular time and variability) that reflect real-time sensorimotor integration under distraction.
- Operational Value: Standardized simulation conditions allow for high-reproducibility screening of behavioral responses to environmental modulators.
Translational & Preclinical Research
- Scientific Value: Captures concurrent physiological (heart rate) and subjective (mood) metrics, enabling multimodal assessment of internal state during task performance.
- Operational Value: Supports translational continuity by linking subjective experience and autonomic response to observable behavior in a controlled setting.
Pipeline & Workflow Integration
The method fits within early discovery workflows where behavioral phenotyping informs target validation and mechanistic de-risking before lead identification.
- Discovery Biology: Supports hypothesis testing on how environmental stimuli affect neural circuits governing attention and motor control.
- Screening: Delivers standardized, quantifiable behavioral outputs enabling cross-condition comparison in distraction models.
- Analytics: Provides continuous behavioral and physiological time-series data for assessing stability and variability of responses.
- Translational Research: Bridges lab-based behavioral assessment with real-world relevance through ecologically valid simulation.
- Enterprise Reuse: Platform can be adapted to test other sensory or pharmacological modulators of behavior across discovery programs.
Operational & Enterprise Impact
- Scientific Value: Improves predictive confidence by isolating the effect of specific stimuli on behavior while controlling for confounder variability.
- Operational Value: Ensures reproducibility through fixed simulation parameters, standardized instructions, and counterbalanced condition presentation.
- Strategic Value: Enables early detection of behavioral liabilities or enhancements from environmental exposures, informing risk assessment.
- Portfolio Impact: Supports go/no-go decisions by quantifying how non-target factors influence functional outcomes in behavioral assays.
Implementation Considerations
- Requires expertise in behavioral neuroscience, simulation operation, and physiological monitoring.
- Dependent on driving simulator hardware, synchronized audio-visual presentation, and biometric data acquisition systems.
- Necessitates cross-team alignment between behavioral scientists, engineers, and data analysts for protocol standardization.
- Must account for individual differences in simulator sensitivity, motion sickness susceptibility, and baseline driving experience.
- Limited to assessing car-following behavior; does not capture complex driving scenarios like urban navigation or emergency response.
