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Truck drivers are often criticised for being too rash and erratic in their driving, and it has been proven several times in the past that, their driving has indeed cause many deaths. These drivers and their profession though, are absolutely necessary for survival of civilisation because of the logistical support they provide to us.
The one way to improve their driving is to constantly monitor and correct them, but how would a large number of trucks be monitored? This is exactly where Netradyne and its product, Driveri come into the picture.
Netradyne is a Bangalore-based tech company that has developed a camera feed-based artificial intelligence platform with an intention to reform commercial vehicle driver recognition and fleet safety.
The company was founded in 2015 and almost immediately started development of Driveri - A connected, intelligent camera for commercial vehicles. Driveri, (pronounced Driver-Eye) combines artificial intelligence with a multiple-angle, high-definition video-stream.
The implementation of AI provides meaningful and critical data to commercial fleet operators by detecting, reasoning and predicting incidents if any through driver behaviour. This helps the operators in recognising superior performance and also addressing dangerous driving before it turns into a mishap.
DriveSpark recently had an opportunity to speak with Teja Gudena, the Vice President, Engineering - Devices, of Netradyne. We got talking about the company and it's products. Here is the conversation.
Tell us about Netradyne.
Our founders, Avneesh Agrawal and David Julian, both with several years of experience in the technology industry, started Netradyne to revolutionise vision-based deep learning.
Vision based deep learning has multiple applications, of which smart surveillance is one. The debate was on whether we should start off with smart surveillance or in the automotive sector, and we zeroed in on the auto sector.
Netradyne was started with a focus on the American market, and the commercial transportation fleets there required a reliable, high-tech monitoring system that we were happy to provide.
Since its inception, Netradyne has been focusing on implementation of AI into auto sector? Why Auto Sector?
The auto sector was our first product because it turned out to be feasible through market studies we had conducted. Surveys and studies showed that AI implementation in the auto sector is a largely untapped market, and this became our strength.
Your product, Driveri has been targeted at the commercial vehicle sector. Why not private operators and personal car users?
The main reason which goes against the implementation of Driveri in personal vehicles is the cost of the device.
Also, the application of the device for personal users will be different. It will be implemented with safety in focus and insurance companies will be able to settle claims now easily. Insurance premium too can be set based on the driving style of the individual which can be ascertained by using data generated by Driveri.
Why aren't taxi fleets adopting Driveri?
Driveri for taxi fleets will have to be modified to accommodate various other factors including the fare and also safety of the passenger. Hence, a seperate model will have to be worked out for taxi fleets.
Do you see any other uses for Driveri than just improving fleet management? Like, maybe increased safety?
Safety definitely is one of the most important factors, as Driveri streams videos from multiple angles, and the footage is not just in the vehicle, it can be retrieved from the server. Hence, it can be used as a form of smart surveillance.
As an example, if it is implemented in taxi fleets that drop employees home after a night shift, the employees, and the drivers' safety can be monitored. The device can also be modified and programmed for facial recognition, number plate recognition, etc.
There is footage showing Driveri's trials in America. But were there any trials in India?
The Algorithms used in Driveri were designed for the American market. However, thereahev been a few mainstream manufacturers from India too, that have been testing Driveri on a trial basis.
Are there any challenges that are specific to India?
The biggest challenge of course are the road conditions here. Devices are programmed to recognise lanes using the white markings on the road as a reference point. Most roads in India do not have this. For this specific challenge we are trying to program the device into plotting its own lanes even on unmarked roads, and we will get there.
Another major challenge in India is the fragmented commercial fleet network. There are hundreds of operators with small fleets. The biggest fleet operator in India runs several hundreds of vehicles, while the small ones operate even single digits. Driveri on the other hand is designed for bigger fleets.
How are the drivers monitored in a larger fleet of a few hundred vehicles? Surely, a few hundred videos cannot be streamed at once.
This is easy as the fleet management only receives a notification when the driver makes an error, like jumping a signal or speeding. Driveri also features a green zone which helps recognise the good driving habits of the driver, enabling the fleet management to appreciate him/her.
Devices like Driveri not only help increase safety for the drivers, but also for pedestrians and other road users. Implementation of this technology in India seems like a challenge at the moment, which we hope is sorted out over the next few years.