AIフリートマネジメントシリーズ Part 1：運転行動 vs 車両コンディション

Data shows that the risk level for commercial fleets or professional drivers is several times higher than that of general drivers (this can be objectively compared through accident rates and insurance premiums). Let me first clarify that this is not entirely because professional drivers are worse. If you drive on the road for 8-10 hours a day, need to park by the roadside dozens of times, get in and out of the vehicle to load or unload, or let passengers on and off, and on top of that, deal with complaints from bosses or customers about late deliveries, your record might not look so good either.

However, the data is clear: poor or even dangerous driving behavior is a long-standing issue that any commercial fleet must face and resolve. Where there is demand, there will be supply—this is an unchanging law of market economics. As a result, various products and technologies for detecting and monitoring driving behavior flood the market, many of which claim to be backed by scientific evidence. Just like various health supplements, they not only always receive certification from some unheard-of authoritative British institution but also often claim to be the result of years of effort by doctors or professors from National Taiwan University.

The effectiveness of many fleet management technologies is as inscrutable as the medical effects of these supplements. While data itself does not lie, those who select and interpret the data might. This time, let’s explore two indicators that are often valued in both the Taiwanese and Western markets: Harsh braking and Harsh acceleration.

From the perspective of the average person, harsh braking or acceleration is indeed a negative indicator of driving behavior. For commercial fleets, it may lead to frequent accidents or unnecessary fuel consumption, making it a good basis for fleet management. Products that obtain these two data points using OBD, CAN, or gyro/accelerometer are as numerous as fish in the sea.

To conclude: if you’re hesitating about whether to bring an umbrella, just look at the probability of rain, not the height or thickness of the clouds. While understanding cloud information is helpful, looking at the probability of rain is more direct and effective.

Back to the topic, when do people usually brake harshly? Aside from professional tofu delivery drivers like Takumi Fujiwara, it’s usually when following too closely (poor driving), being aggressive or being aggressed upon (the former is poor driving, the latter is helplessness), or encountering unexpected obstacles on the road (emergency defensive driving). Can harsh braking data distinguish between poor, helpless, or emergency defensive driving? Maybe, but it’s like observing cloud height and thickness—not direct or effective. Is there a more direct and effective method? Yes—direct video analysis.

When do people usually accelerate harshly? Typically, it’s to beat a red or yellow light (poor driving), aggressive driving (poor driving), maintaining speed uphill (normal driving), or merging into fast traffic (normal driving). Can harsh acceleration data distinguish between poor and normal driving? Maybe, but it’s not direct or effective. Is there a more direct and effective method? Yes—direct video analysis.

You might say that video analysis can’t analyze fuel consumption, right? From my understanding of various fuel consumption analysis methods, I’ve never seen one using video, because it’s not direct or effective. But can harsh braking or acceleration analyze fuel consumption? Maybe, but isn’t it simpler and more direct to use the total mileage/total fuel consumption figures for a single trip, day, week, or month? Isn’t this elaborate process similar to deciding whether to bring an umbrella by looking at cloud height or thickness?