Car accidents are scary for even the most experienced Maryland driver. With how dangerous car accidents can be, a lot of science has been devoted to figuring out how to prevent crashes.
MIT has developed a deep learning model using artificial intelligence to predict high crash risk areas on maps. These maps are high resolution and very detailed, able to narrow down the detail to exact highways and roads.
How does deep learning help avoid crashes?
One of the students behind the deep learning model said that by capturing where car accidents are more likely to happen, researchers can better understand the contributing factors to crashes in that area. For example, if a highway on-ramp is at high risk for car accidents, then researchers can analyze the traffic patterns in that area.
Governments can use the data to improve new routes going forward and make improvements to these high-risk areas. While improvements take time, it’s worth investing in making certain roads and highways safer for drivers of all types.
For the short term, adding this data onto apps like Google Maps and Waze might encourage drivers to drive with caution through high-risk areas. These alerts can come in the form of a pop-up before people are about to drive into the high-risk area or a warning before they start their trip.
Why does it matter?
Car accidents might be rare for people on an individual basis, but overall, they’re still the leading cause of death in children and young adults. This is especially true for residents of states like Maryland who are more likely to face inclement weather that contributes to dangerous driving conditions.
Motor vehicle accidents can have a devastating financial and emotional impact on victims, but most people are expected to drive everywhere. It’s hoped that this sort of technology can help reduce car accidents in the future.