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BSquared White-Paper Series: Reliable Datasets, Consistency, and The Effects of Late Braking


The past couple weeks have been extremely busy for us! With nonstop one-on-one conversations, we’ve been learning more about the community that makes up grassroots motorsports. We’ve also been getting requests for us to provide feedback on your track data! It’s a lot of fun looking through the data given to us by our interviewees, and we love offering some free value at the same time. As an example, here is a case study of some data that was logged through RaceChrono with an external 10Hz GPS and OBD/CAN data. The vehicle was a Toyota 86 at Canadian Tire Motorsports Park Grand Prix Track. With Track Attack, we can visualize the data. Let’s get started!


Data Issues:


The very first thing to consider is the quality of the data. For instance, we don’t know how the driver mounted their data system or what kind of calibration procedures have been done. This information is important as it allows us to make the appropriate assumptions when looking at the data. Otherwise, incorrect data can lead to incorrect conclusions.

As an example, the accelerometer data given to us was not reliable. The data was uncalibrated before the start of the lap and there are a lot of vibrations in the data. You can smooth the data by applying functions available on Excel or lap data processing applications, however this reduces the accuracy of your data. In this case, where the data system was a mounted phone, it’s better to just invest in better mounting.


Accelerometer data can be extremely valuable if it is calibrated and mounted properly. Seen below, you can see that for some reason the driver seems to be pulling 0.2 to 0.3 g on the main straight. This should not be the case. This offset can sometimes be fixed in the analysis stage if your data visualization tool has this capability. AiM and MoTeC both have this capability in their software. Drivers should focus on positioning the sensor on an axis similar to that of the car, meaning the sensor should not be tilted toward the driver, it should be pointed matching the car’s direction.

An example of an accelerometer that is not calibrated properly. Note that it is reading ~0.25g on the straight.
Figure 1: An example of an accelerometer that is not calibrated properly. Note that it is reading ~0.25g on the straight.
An example of an accelerometer that is calibrated properly. Note that it is reading ~0g laterally on the straight.
Figure 2: An example of an accelerometer that is calibrated properly. Note that it is reading ~0g laterally on the straight.

We can tell that calibration has not been done because both lateral and longitudinal acceleration are peaking at the exact time. Think about this from a vehicle perspective, the peak braking force is seemingly applied at the same time as your peak lateral force. The main reason for this is the axis of the internal accelerometer is not aligned to the vehicle axis and therefore, braking forces are being picked up as lateral forces.


Lateral and longitudinal acceleration peaking at the same time is very unlikely to happen on track. This is most likely the result of an uncalibrated accelerometer.
Figure 3: Lateral and longitudinal acceleration peaking at the same time is very unlikely to happen on track. This is most likely the result of an uncalibrated accelerometer.

Consistency


Consistency is measured by overlaying multiple subsequent laps from the same driver and vehicle. Typically, vehicle speed is used to evaluate consistency. The more each speed trace deviates from each other, the worse the consistency.

Vehicle speed from 4 laps superimposed on top of each other.
Figure 4: Vehicle speed from 4 laps superimposed on top of each other.

Data can tell you a lot of information about the vehicle. But how do you know if the problem is the vehicle or the driver? One way is to check the consistency of the data. In fact, this is the very first thing we check for. If there are consistency issues, we will report on these issues and may not look any further. The reason is, if a driver is consistently inconsistent, it’s very difficult, if not impossible, to make accurate conclusions that would help develop and improve a driver.


Based on the driver’s throttle and brake position data, we can see that inconsistency with Turn 4 is the worst. The driver is trying to figure out whether they should brake or coast here and that mixed with fear is preventing them from being consistent. Note that this is completely normal on this track, Turn 4 and Turn 2 are the most terrifying corners so work needs to be done to combat the fear.

Throttle position and brake position for Turn 4 and 5. Note the inconsistency.
Figure 5: Throttle position and brake position for Turn 4 and 5. Note the inconsistency.

Some experimentation will be needed to figure out what is best for this driver, but they must prioritize being patient and smooth on throttle after coasting. This is because they are struggling with steering oscillation every time they get on the throttle. They need to pick a point to be off throttle, brake if necessary, then be patient before being on the throttle.

The inconsistencies from Turn 4 also seem to be affecting Turn 5. The red line here was this driver’s fastest lap and that is where they applied the brakes earlier, carried speed, and had a smoother throttle on.


Turn 5 and slow corners, generally, are areas where this driver can experiment. Keep braking early like the driver did in their fastest lap (red line) but keep the foot on the brake with some pressure into the corner. Even if it’s just 5% pressure, this can make sure that the car is loaded properly on corner entry. Clearly, the driver tries out different braking points in some laps, but seems to perhaps overdo it. Remember that brake release should be the opposite of brake aggression, smooth on the release.

Keeping your foot on the brakes and braking early will feel slow initially, but the goal is to get into the mindset and habit of making sure the car is loaded correctly on corner entry. Once this is mastered, the driver can work up the courage slowly to push the braking point.


Now let’s look at the driver’s steering. The steering angle at Turn 5a and 5b could be more consistent. There is a 30 degree change in steering angle here which could be minimized (not necessarily eliminated) by getting the right braking point into Turn 5a and making sure the line is good. The driver can experiment here and see if this corner can be turned into one big corner to remove the braking in between.



Figure 6: Steering angle in Turn 5.Figure 6: Steering angle in Turn 5.

It’s clear that the driver is pushing hard, and massive credit given where it’s due! There is enough information here for us to go on with analysis, but at the end of the day - Turn 4 and Turn 5 need to be mastered for this driver. Concentrating on consistency and building confidence will improve overall lap time. Vehicle stability could be another issue since corner exit seems to trouble the driver quite a bit, but focus on being consistent first and then the vehicle issues will become obvious.


Conclusion


The key takeaways here are the importance of reliable data, consistency, and the effects of late braking. We delve further into these topics in our Applied Motorsports Data Course. Join us as we discuss specific steps to setting up the data acquisition hardware and prepare the mental state of the driver to collect reliable data. We have designed drills and study sheets for drivers to take to the track to validate their consistency. We also go over exercises to eliminate bad habits, such as excessive late braking, for all participants to practice at the track.



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