Initially being applied to roadside security -- such as stopping car bombs or drugs smuggling -- UVeye's tech claims to be able to analyse any vehicle from underneath to identify and detect threats that would otherwise be concealed to the human eye, even as it is moving, up to 28 MPH, apparently. We are the first to introduce a machine learning vehicle inspection system that detects anomalies in any vehicle while in motion within three seconds using advanced image processing and audio recordings". "In the era of Mobility-as-a-Service, companies such as car rental companies, fleets, car dealerships, vehicle repair shops, and OEMs all rely on seamless vehicle operation. UVeye's machine learning system can detect vehicle leaks, wear and tear, and any damages that would previously go unnoticed.
Traditional intelligent algorithms generally use shallow learning models to handle situations with large amounts of data in complex classifications. Some of the most direct benefits that deep learning algorithms can bring include achieving comparable or even better-than-human pattern recognition accuracy, strong anti-interference capabilities, and the ability to classify and recognise thousands of features. With this large amount of quality training data, human, vehicle, and object pattern recognition models will become more and more accurate for video surveillance use. The deep learning model requires a large amount of samples, making a large amount of calculations inevitable.
How is predictive data changing the automotive industry and what changes can we expect to see in the future? Connected and autonomous cars are going to benefit most from the inclusion of predictive data because their design centers on data collection and processing. As more and more connected cars hit the roads, data management is going to become an essential tool. Predictive data has already shown potential for preventative maintenance, but this same application could be used to predict software problems and security flaws as well.
This year artificial intelligence (AI) with the associated technologies such as smart IoT sensors and increasingly powerful and seamless human machine interfaces (HMIs) proved to be the cynosure of all eyes that passed by. Most global automobile companies are working on driverless cars that are based on continuous advances in computer vision and deep learning technologies. This is another example where smart sensors and human machine interactions, when combined with artificial intelligence technologies could create tangible advances in the way we drive, work and play. For instance, Cisco is working with Hyundai to create a strong network backbone for their vehicles that would help Hyundai to simplify its network and seamless connect to other vehicles, through the cloud.
The Internet of Things (IoT) and Machine Learning are two of the hottest technologies of our time. At first glance, I really like some of the ideas that are being proposed by the combination of Internet of Things and Machine Learning: smart light bulbs that know when to turn themselves on and off; smart kettles that will make sure you'll have freshly made coffee at the exact time you want it without having thought about it; smart fridges that will do the grocery for you; smart locks that will recognize you and unlock with the tap of a phone. I don't have to worry about leaving the door unlocked because my smart lock will automatically lock the door when it senses that the house is empty. Combined with the power of the fast evolving VR technology, IoT will enable us to travel to distant locations, feel things, meet people and do a lot more without ever setting foot outside our homes.