Blink patterns in Formula car driving
Research Digest
Author(s): Neil M. Thomas, Nadia Paraskevoudi
October 26, 2023
Photo by Jesper Giortz-Behrens on Unsplash
Photo by Jesper Giortz-Behrens on Unsplash
Photo by Jesper Giortz-Behrens on Unsplash
How does eyeblink timing affect driving performance?
Formula racing is the pinnacle of high-speed motorsport, where drivers push cutting-edge, open-wheel race cars to their limits. In the fast world of Formula racing, cars can go over 320 kilometers per hour, while navigating sharp turns and tough straightaways.
In such a high-pressure environment, quick thinking isn’t just important—it’s essential. Hence, it is crucial to understand the factors influencing drivers’ decision-making and reaction times to optimise racing performance.
On average, a human eye blinks approximately 20 times per minute. Each blink can result in a loss of approximately 200 ms of visual information. Suboptimal blink timing could therefore have a significant impact on racing results.
While previous eye tracking research studied the relationship between blink behaviour and cognitive states during simulated driving. Studies on blink behaviour during real-world driving scenarios are limited.
Examining eyeblink behaviour in real-world Formula car driving
In a recent study in iScience, Nishizono and colleagues looked for links between blink behaviour and car control. Their study focused on analysing the blink patterns of three highly skilled racing drivers during real-world practice sessions.
The goal was to assess the blink patterns for each lap during different race phases, such as cornering or overtaking. The eye blink behaviour of the different drivers shows repeatable patterns in certain phases of a race. This could provide evidence for race-specific blink strategies adopted by the drivers.
To do this, they used our wearable, head-mounted, eye tracking system Pupil Core. Their data collection system recorded different factors about the car's performance, like speed and acceleration (Figure 1). With this setup, they could find blink patterns during the lap progression. They also looked into how these patterns might connect to driving performance.
Figure 1. A) The eye-tracking cameras were installed inside the helmet. B) Video images from Pupil Core and car telemetry data were used to calculate the blink distance from the start/finish line which was then normalised to the maximum travelled distance in the lap.
To quantify blink patterns, the researchers used so-called SPIKE-distance. This statistical metric was originally created to study differences in timing of neuron activity. It compares the similarity and dissimilarity of event sequences, like neuron spikes or eyeblinks. In this study, the SPIKE-distance reduces as event sequences become more synchronised (as illustrated in Figure 2, left).
How did the authors use this metric?
The authors first calculated the SPIKE-distance values for each driver and course combination and tested them against a null distribution of permutations. This allowed them to determine whether the observed SPIKE-distance values were statistically significant and not due to chance. Ultimately, this testing aimed to investigate the hypothesis that eyeblinks might serve as indicators of reproducible behavioural patterns during Formula driving.
Reproducible eyeblink patterns
The researchers found that the eyeblink patterns of the three formula car drivers were highly reproducible across multiple laps and races. In other words, the drivers each exhibited consistent eyeblink behaviour patterns during each drive (see Figure 2, right).
Figure 2. Left: Smaller vs. larger SPIKE distance trains reflect more vs. less synchronised event trains. Right: Blink patterns are highly reproducible among drives across multiple laps.
Blinking likelihood relates to driving performance
The researchers identified key factors connecting the drivers' blinking patterns with an increase of performance . They found that the moment-by-moment blinking likelihood correlates with the driver's pace. More synchronised and pronounced blink patterns correlate with faster lap times (Figure 3).
The frequency of eyeblinks significantly reduces, when the car was slowing down significantly or taking sharp turns with increased lateral acceleration (Figure 4, region 1 and 2). This finding suggests that the drivers were adapting their blink patterns to the demands of the track and their driving performance. Effectively suppressing blinks during critical moments requiring hyper focus (e.g. the beginning of the race, during cornering, overtaking, and battling with other racers).
Figure 3 - Eyeblink rate and lap time length correlate (left) and blink trains are more synchronised during faster than slower laps (right). Figure 4 - Eyeblink suppression related to car acceleration.
Implications for driving performance and safety
By combining eye tracking with car telemetry data, Nishizono and colleagues show blink suppression during key phases of racing that coincide with very important manoeuvres. Secondly, these patterns were directly linked to racing performance, with more synchronized blink patterns during laps that were completed faster.
These findings suggest that training programs that improve eyeblink control and cognitive states may enhance high-level driving performance and safety, and also open up possibilities for developing real-time monitoring systems that detect driver fatigue or distraction in race car drivers.
Key highlights
Eyeblink patterns during formula driving were highly reproducible across multiple laps and races.
Faster lap times correlated with more synchronised and pronounced blink patterns.
Blinks were suppressed during critical moments requiring heightened focus, that is when the car was accelerating laterally (e.g., battling with other racers).
You can read the full article here: https://www.cell.com/iscience/fulltext/S2589-0042(23)00880-5
If you wish to include your published work or research projects in future Research Digests, please reach out!
How does eyeblink timing affect driving performance?
Formula racing is the pinnacle of high-speed motorsport, where drivers push cutting-edge, open-wheel race cars to their limits. In the fast world of Formula racing, cars can go over 320 kilometers per hour, while navigating sharp turns and tough straightaways.
In such a high-pressure environment, quick thinking isn’t just important—it’s essential. Hence, it is crucial to understand the factors influencing drivers’ decision-making and reaction times to optimise racing performance.
On average, a human eye blinks approximately 20 times per minute. Each blink can result in a loss of approximately 200 ms of visual information. Suboptimal blink timing could therefore have a significant impact on racing results.
While previous eye tracking research studied the relationship between blink behaviour and cognitive states during simulated driving. Studies on blink behaviour during real-world driving scenarios are limited.
Examining eyeblink behaviour in real-world Formula car driving
In a recent study in iScience, Nishizono and colleagues looked for links between blink behaviour and car control. Their study focused on analysing the blink patterns of three highly skilled racing drivers during real-world practice sessions.
The goal was to assess the blink patterns for each lap during different race phases, such as cornering or overtaking. The eye blink behaviour of the different drivers shows repeatable patterns in certain phases of a race. This could provide evidence for race-specific blink strategies adopted by the drivers.
To do this, they used our wearable, head-mounted, eye tracking system Pupil Core. Their data collection system recorded different factors about the car's performance, like speed and acceleration (Figure 1). With this setup, they could find blink patterns during the lap progression. They also looked into how these patterns might connect to driving performance.
Figure 1. A) The eye-tracking cameras were installed inside the helmet. B) Video images from Pupil Core and car telemetry data were used to calculate the blink distance from the start/finish line which was then normalised to the maximum travelled distance in the lap.
To quantify blink patterns, the researchers used so-called SPIKE-distance. This statistical metric was originally created to study differences in timing of neuron activity. It compares the similarity and dissimilarity of event sequences, like neuron spikes or eyeblinks. In this study, the SPIKE-distance reduces as event sequences become more synchronised (as illustrated in Figure 2, left).
How did the authors use this metric?
The authors first calculated the SPIKE-distance values for each driver and course combination and tested them against a null distribution of permutations. This allowed them to determine whether the observed SPIKE-distance values were statistically significant and not due to chance. Ultimately, this testing aimed to investigate the hypothesis that eyeblinks might serve as indicators of reproducible behavioural patterns during Formula driving.
Reproducible eyeblink patterns
The researchers found that the eyeblink patterns of the three formula car drivers were highly reproducible across multiple laps and races. In other words, the drivers each exhibited consistent eyeblink behaviour patterns during each drive (see Figure 2, right).
Figure 2. Left: Smaller vs. larger SPIKE distance trains reflect more vs. less synchronised event trains. Right: Blink patterns are highly reproducible among drives across multiple laps.
Blinking likelihood relates to driving performance
The researchers identified key factors connecting the drivers' blinking patterns with an increase of performance . They found that the moment-by-moment blinking likelihood correlates with the driver's pace. More synchronised and pronounced blink patterns correlate with faster lap times (Figure 3).
The frequency of eyeblinks significantly reduces, when the car was slowing down significantly or taking sharp turns with increased lateral acceleration (Figure 4, region 1 and 2). This finding suggests that the drivers were adapting their blink patterns to the demands of the track and their driving performance. Effectively suppressing blinks during critical moments requiring hyper focus (e.g. the beginning of the race, during cornering, overtaking, and battling with other racers).
Figure 3 - Eyeblink rate and lap time length correlate (left) and blink trains are more synchronised during faster than slower laps (right). Figure 4 - Eyeblink suppression related to car acceleration.
Implications for driving performance and safety
By combining eye tracking with car telemetry data, Nishizono and colleagues show blink suppression during key phases of racing that coincide with very important manoeuvres. Secondly, these patterns were directly linked to racing performance, with more synchronized blink patterns during laps that were completed faster.
These findings suggest that training programs that improve eyeblink control and cognitive states may enhance high-level driving performance and safety, and also open up possibilities for developing real-time monitoring systems that detect driver fatigue or distraction in race car drivers.
Key highlights
Eyeblink patterns during formula driving were highly reproducible across multiple laps and races.
Faster lap times correlated with more synchronised and pronounced blink patterns.
Blinks were suppressed during critical moments requiring heightened focus, that is when the car was accelerating laterally (e.g., battling with other racers).
You can read the full article here: https://www.cell.com/iscience/fulltext/S2589-0042(23)00880-5
If you wish to include your published work or research projects in future Research Digests, please reach out!
How does eyeblink timing affect driving performance?
Formula racing is the pinnacle of high-speed motorsport, where drivers push cutting-edge, open-wheel race cars to their limits. In the fast world of Formula racing, cars can go over 320 kilometers per hour, while navigating sharp turns and tough straightaways.
In such a high-pressure environment, quick thinking isn’t just important—it’s essential. Hence, it is crucial to understand the factors influencing drivers’ decision-making and reaction times to optimise racing performance.
On average, a human eye blinks approximately 20 times per minute. Each blink can result in a loss of approximately 200 ms of visual information. Suboptimal blink timing could therefore have a significant impact on racing results.
While previous eye tracking research studied the relationship between blink behaviour and cognitive states during simulated driving. Studies on blink behaviour during real-world driving scenarios are limited.
Examining eyeblink behaviour in real-world Formula car driving
In a recent study in iScience, Nishizono and colleagues looked for links between blink behaviour and car control. Their study focused on analysing the blink patterns of three highly skilled racing drivers during real-world practice sessions.
The goal was to assess the blink patterns for each lap during different race phases, such as cornering or overtaking. The eye blink behaviour of the different drivers shows repeatable patterns in certain phases of a race. This could provide evidence for race-specific blink strategies adopted by the drivers.
To do this, they used our wearable, head-mounted, eye tracking system Pupil Core. Their data collection system recorded different factors about the car's performance, like speed and acceleration (Figure 1). With this setup, they could find blink patterns during the lap progression. They also looked into how these patterns might connect to driving performance.
Figure 1. A) The eye-tracking cameras were installed inside the helmet. B) Video images from Pupil Core and car telemetry data were used to calculate the blink distance from the start/finish line which was then normalised to the maximum travelled distance in the lap.
To quantify blink patterns, the researchers used so-called SPIKE-distance. This statistical metric was originally created to study differences in timing of neuron activity. It compares the similarity and dissimilarity of event sequences, like neuron spikes or eyeblinks. In this study, the SPIKE-distance reduces as event sequences become more synchronised (as illustrated in Figure 2, left).
How did the authors use this metric?
The authors first calculated the SPIKE-distance values for each driver and course combination and tested them against a null distribution of permutations. This allowed them to determine whether the observed SPIKE-distance values were statistically significant and not due to chance. Ultimately, this testing aimed to investigate the hypothesis that eyeblinks might serve as indicators of reproducible behavioural patterns during Formula driving.
Reproducible eyeblink patterns
The researchers found that the eyeblink patterns of the three formula car drivers were highly reproducible across multiple laps and races. In other words, the drivers each exhibited consistent eyeblink behaviour patterns during each drive (see Figure 2, right).
Figure 2. Left: Smaller vs. larger SPIKE distance trains reflect more vs. less synchronised event trains. Right: Blink patterns are highly reproducible among drives across multiple laps.
Blinking likelihood relates to driving performance
The researchers identified key factors connecting the drivers' blinking patterns with an increase of performance . They found that the moment-by-moment blinking likelihood correlates with the driver's pace. More synchronised and pronounced blink patterns correlate with faster lap times (Figure 3).
The frequency of eyeblinks significantly reduces, when the car was slowing down significantly or taking sharp turns with increased lateral acceleration (Figure 4, region 1 and 2). This finding suggests that the drivers were adapting their blink patterns to the demands of the track and their driving performance. Effectively suppressing blinks during critical moments requiring hyper focus (e.g. the beginning of the race, during cornering, overtaking, and battling with other racers).
Figure 3 - Eyeblink rate and lap time length correlate (left) and blink trains are more synchronised during faster than slower laps (right). Figure 4 - Eyeblink suppression related to car acceleration.
Implications for driving performance and safety
By combining eye tracking with car telemetry data, Nishizono and colleagues show blink suppression during key phases of racing that coincide with very important manoeuvres. Secondly, these patterns were directly linked to racing performance, with more synchronized blink patterns during laps that were completed faster.
These findings suggest that training programs that improve eyeblink control and cognitive states may enhance high-level driving performance and safety, and also open up possibilities for developing real-time monitoring systems that detect driver fatigue or distraction in race car drivers.
Key highlights
Eyeblink patterns during formula driving were highly reproducible across multiple laps and races.
Faster lap times correlated with more synchronised and pronounced blink patterns.
Blinks were suppressed during critical moments requiring heightened focus, that is when the car was accelerating laterally (e.g., battling with other racers).
You can read the full article here: https://www.cell.com/iscience/fulltext/S2589-0042(23)00880-5
If you wish to include your published work or research projects in future Research Digests, please reach out!