How can eye tracking predict driving risk through empathic responses?

In a recent study published in Transportation Research Part F, the team of Associate Professor Ma Jinfei from Liaoning Normal University, in collaboration with Associate Professor Chen Jingxi from Tongji University, used Tobii eye tracking technology to successfully translate abstract empathic responses into measurable visual indicators.

From empathy as a trait to empathy as a state response 

Previous studies have often treated empathy as a stable personality trait, measured primarily through questionnaires or scales. But in real driving environments, driver behavior is highly context-specific: someone who is gentle in everyday life may lose empathy in an instant when road rights are challenged. 

The research team believes that accurately understanding the relationship between empathy and traffic safety requires a distinction between everyday empathic traits and empathy responses in driving states. To move beyond the social desirability bias often found in self-report measures, the team used visual attention patterns as an objective way to measure momentary empathic responses. 

The study focused on empathic attention bias: when people see others in pain, such as images of painful stimuli, how does the brain automatically allocate attention? Higher empathy is often associated with safer driving behavior. The key question is how to measure this momentary psychological response reliably in real time. 

Measuring empathic attention with eye tracking 

The team recruited drivers from different backgrounds with high and low empathic traits and used a dot-probe paradigm combined with eye tracking. Participants first watched videos showing conflicts over road rights, such as aggressive overtaking or reckless driving. They were then shown paired images of painful and neutral stimuli. 

Using Tobii Pro Spectrum (600 Hz) screen-based eye tracker, the team precisely captured participants’ visual trajectories as they viewed these images.

The analysis focused on two core dimensions:

• Attentional orienting: reflected by the start time of the first fixation.

• Attentional maintenance: reflected by the total fixation duration.

In other words, the study examined whether drivers could quickly detect others’ pain and how strongly their attention remained on pain-related cues. 

Eye movement data predicts traffic violation points 

The results revealed a close relationship between visual attention and driving behavior. Drivers with higher empathic traits showed significantly longer fixation durations on painful stimuli. This suggests that they not only detected others’ pain more readily but also showed stronger attentional maintenance toward these signals.

More importantly, regression analysis showed that drivers’ total fixation duration on others’ pain was significantly and negatively correlated with their actual traffic violation points. Put simply, the more attention a driver paid to others’ pain in the experiment, the fewer traffic violations they tended to show in real life. 

Reducing road rage through in-vehicle guidance 

After revealing a possible cognitive mechanism underlying traffic violations, the research team further explored targeted intervention methods for road safety. A second experiment examined the potential of an Intelligent Driving Companion (IDC) system and tested whether this smart interaction approach could enhance drivers’ empathic responses in real time. 

Through contextualized voice guidance, the IDC system reframed another driver’s reckless behavior as an unintentional act, such as a lack of experience. This significantly increased low-empathy drivers’ attention to painful images. The findings suggest that real-time in-vehicle guidance may help strengthen empathic responses and reduce emotionally driven risky driving behavior. 

Toward safer smart mobility 

By linking eye movement indicators to real-world traffic violation points, Ma Jinfei’s team provides a new perspective for traffic safety research. Their work also demonstrates the value of eye tracking in building smart mobility systems with a more human-centered approach. 

In the future, such visual attention indicators may support driver-state assessment and the design of in-vehicle interventions. Rather than relying solely on driver reports, in-vehicle systems could use objective signals of attention and emotion to identify risk earlier and respond more adaptively. 

About the research group 

Liaoning Normal University’s Safety Psychology Research Group in the
School of Psychology primarily focuses on drivers’ safety responsibility awareness and driving performance in human-machine interaction contexts. Their research emphasizes the assessment and intervention of drivers’ rule awareness, situational awareness, attention allocation, and hazard perception in these contexts. Building on this foundation, as intelligent technologies have advanced, the team has gradually expanded its research scope to include human-machine interaction issues in autonomous driving and complex system operation contexts. They investigate how factors such as operators’ overtrust, diffusion of responsibility, attentional quality, and interface design influence decision-making performance. 

References 

Ma, J., Li, G., Ding, J., Fang, D., & Chen, J. (2026). Predictive role of empathic attention bias on driver traffic violations and the empathy-enhancing effects of intelligent driving companionship. Transportation Research Part F: Traffic Psychology and Behaviour, 119, 103568.