Protect Yourself With A Dash Cam
We are all used to watching videos from dashboard cameras and while many come from Russia they originated in Asia to help drivers deal with other road users and ensuring they were covered if they were involved in an accident.
We are all used to watching videos from dashboard cameras and while many come from Russia they originated in Asia to help drivers deal with other road users and ensuring they were covered if they were involved in an accident. To help drivers in the States, they are being used to ensure they don’t lose their income if they work on the road.
No longer are cameras only used for racing cars, but the general public. Motorbike riders wear them on their helmet. The Police are using them to prove driving violations. There are several hours of footage recorded through the windscreen and then overwritten by the next incidents.
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Older models show date and time while new ones have GPS tagging. Speed is recorded and then can connect via Wi-Fi. According to Paul Veillon – an attorney working with insurance companies – believes that they will prevent insurers deflecting fault away from their client. The camera can show speed, who was at fault in lane changes and can even show impact times when multiple vehicles are involved. Force of impact can be shown, and again this prevents insurance companies claiming a large crash is just a bump.
The cameras became popularity as a result of on road cameras proving liability. There are few problems with dash cams although in some cases in car audio has to be switched off. It is also frowned upon to film the Police. Little space is needed to install the camera, so windscreens are not obscured. Cadillac offers a system that saves incidents to an SD card.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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