Reimagining road safety with Sentiance’s AI-driven Crash Detection

Protect your customers and their loved ones

Car accidents are sadly one of the leading causes of death. Fast response times can make all the difference in preventing fatal injuries. In fact, according to the World Health Organization, road traffic injuries are currently estimated to be the 8th leading cause of death across all age groups globally. They are predicted to become the seventh leading cause of death by 2030.

This is why Sentiance developed its Crash Detection solution which utilizes motion insights to detect car crashes and alert emergency services. Let’s explore what this solution can do for you and how it works.

AI-driven Crash Detection

Sentiance’s Crash Detection technology is powered by an on-device AI solution that uses motion data from mobile sensors. Mobile sensors include accelerometers, gyroscopes, and GPS location. The algorithm is complex in itself but easy to integrate the SDK into your app. It is designed to quickly detect when a car has been involved in a high-impact crash.

Once it senses an accident, it checks if you’re okay. It can then initiate the accident management process by alerting your family, emergency services, or even your roadside assistance and insurance company.

Advantages of an on-device solution

So, what is an “on-device solution”? It’s a way of processing motion sensor data directly on your phone, without any data including Personally Identifiable Information (PII) being sent out to the cloud. This lets the system detect crashes in real time and keeps your data private. There are many benefits to this, too! With greater cost efficiency compared to traditional cloud-based solutions, it simplifies and accelerates the scaling process to accommodate more users. 

How does Crash Detection work?

Crash detection begins with identifying unusual patterns in motion data from motion sensors such as the accelerometer sensor data to detect peaks (or G-forces). If these peaks match a particular pattern, they’re regarded as crashes. Contextual checks are carried out to ensure a peak indicates an actual crash. For example, whether the speed before the peak is sufficiently high, or a vehicle suddenly came to a halt seconds after the crash.

Once the model has been ascertained, the signals confirm a crash. A notification is then sent to the enclosing app highlighting the location and time. The confidence level indicates the probability of a crash, while parameters like pre-impact speed and peak acceleration offer insight into its severity.

 Conclusion

In today’s world where speed matters more than ever, having technology like Sentiance’s Crash Detection at your disposal can be invaluable. By leveraging machine learning algorithms to detect car crashes quickly and accurately, Sentiance’s technology helps ensure the safety of your customers’ loved ones while also providing you with a reliable way to manage accident incidents efficiently.

Download the free eBook on Crash Detection & alerting here.