Rules are then written for the computer system to learn about all the data points and make calculations based on the rules of the road. Computer systems are programmed with machine learning algorithms and continuously learn to look at more data more quickly than any human would be able to. It might even notice lots of interactions when "Fly the Friendly Skies" ads are placed next to images of a person being brutally pulled off the plane and place more ads there! Artificial intelligence, machine learning and "self-aware systems" are real.
Basically, machine learning uses algorithms that iteratively learn from data, meaning that it enables computers to find hidden insights without being explicitly programmed where to look. However, where data mining extracts information for human comprehension, machine learning uses it to detect patterns in data and to adjust its program actions accordingly. Incredibly, it's a science that is not new; it is one that was, in fact, predicted nearly 70 years ago by Alan Turing, widely considered the father of theoretical computer science and artificial intelligence. For starters, it is applicable to healthcare, as machine learning algorithms can process more information and spot more patterns than humans can, by several orders of magnitude.
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.
The mid- to long- term development focus include smart remote maintenance service, autonomous driving, smart traffic, and connectivity mobility hub, all of which will benefit from continued R&D investment in the fields of in-vehicle networks, cloud and big data analytics and connected car security technologies. The Swedish safety supplier also announced that it is forming a joint venture with Volvo Cars to develop software for autonomous driving and driver assistance systems. "In order for the system to acquire this information step-by-step, a range of sensors such as radars, cameras, and Surround View systems are needed," said Continental's ADAS business unit head Karl Haupt. Steering on Demand enables the transition between driver and automated driving control through safe, intuitive steering transitions for vehicles capable of SAE Level 3 - 5 automated driving.
It's perhaps understandable therefore that humans struggle to program traffic lights to function effectively. Video games have been enthusiastic adopters of reinforcement learning in recent years, with algorithms used to figure out the most beneficial action at a given moment, and therefore to award the player the highest score. In traffic management, the top score is awarded when drivers are kept waiting for the shortest period of time and the shortness of queues. Like reinforcement learning, deep learning also takes inspiration from the human brain.
For example, existing technologies can detect smartphone Bluetooth radios (for short range) and WiFi radios (for longer ranges) from vehicles as they pass through points where sensor detectors record the cars' presence. Having much greater transparency into traffic flow and congestion points could help city planners identify opportunities to smooth traffic patterns and more accurately plan infrastructure to support their cities' growing needs. Using Swarm Intelligence (SI) algorithms, such as Particle Swarm Optimization (PSO), city planners can also create simulations to understand potential congestion challenges based on how vehicles and pedestrians navigate public spaces. Simulations using real data collected through this mechanism can help city planners determine potential traffic challenges at a highly-granular level--by street, intersection, freeway ramp, school area, etc.--to significantly reduce error rates in planning and address current congestion problems much more quickly.
Telematics company Geotab is using IoT to help fleet management companies significantly reduce accidents. Telematics and Onboard Diagnostics (OBD) are helping fleet management companies and insurance firms collect a wealth of information about vehicles and drivers, including measurable events such as speeding, seatbelt usage, sharp cornering or over-acceleration. IoT sensors and smart cement (cement equipped with sensors) can monitor the structural status of roads and bridges under dynamic conditions and alert us about deficiencies before they turn into catastrophes. The gleaned insights can help in a number of scenarios, including optimizing the use of limited maintenance resources and equipment, as well as predicting and alerting about possible hazards and accidents that may take place because of poor road and weather conditions.