How AI And ML Will Shape The Future of Driving
Reading Time: 4 minutes There’s more to driving than just a car, as infrastructure, people, and even weather conditions contribute to the way we travel. Developers are constantly working on tech that will make travel not only safer but more enjoyable. By 2025, researchers believe the auto artificial intelligence (AI) market will reach a staggering $8.89 billion, which says much about the future of cars and infrastructure. While driverless car technology is already making the rounds and the fully self-driving (FSD) car from Tesla is bound to make its appearance any day, there are some other exciting developments that artificial intelligence and machine learning (ML) have up its sleeve.
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V2V And V2I Communication
In order for FSDs and driverless cars to be fully successful, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication will need to happen. However, V2V and V2I don’t have to wait for SFDs or driverless cars, as it would benefit cars on the road as they are today. This is because this type of communication will alert drivers if another driver has skipped a traffic light or failed to slow down at an intersection, or that they’re nearing a bend or sidewalk. The communication will allow drivers to pay more attention to dangerous or hazardous traffic situations or alert others where there is a breakdown in safety.
Commercial Deliveries Simplified
The autonomous car for everyday use seems to be on our doorstep, but another interesting way AI will affect our lives is with commercial deliveries. Thanks to extensive testing and the help of geo-fenced zones, autonomous deliveries are already in the testing phase. The technology relies on AI and sensors to fulfill online orders, which will simplify the consumer experience. Vehicles that can accommodate multiple loads will ensure that traffic is reduced, which is an important component in reducing emissions and easing congestion in city centers.
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Predictive Technology For Preventative Measures
Predictive driving has already been implemented in many of the newer models, with safety features such as traffic monitors, pedestrian safety, and collision prevention. These features are designed to minimize the risk of an accident. Other predictive driving features that provide drivers with more stability on the road include an automated traction feature, which comes in particularly handy during adverse weather conditions. When it comes to predictive maintenance features, the tire pressure monitor steals the show and will alert drivers when it’s time to get out the jack and change the tire. There are also maintenance features that go beyond the check engine light, as big data and ML work together to be a little more informative when it comes to possible engine problems and maintenance issues before they arise. Currently, this only happens after the fact.
Improving Public Transport Options
While there is already a fair amount of AI and ML present in public transport modes such as trains, ships, and airlines, the potential for expansion will mean greater safety in buses and taxis too. A Boston housing developing is a geo-fenced zone that allows self-driving transport to shuttle passengers to and from a local train station. While this is a small area compared to a larger network of cars on the road, this simple trip can provide much-needed traffic relief to suburban and urban areas that rely on busy pickup and drop off zones. Key cards and tokens could be used to identify passengers and ensure that the monetization aspect is taken care of.
Improved Algorithms For A Better Driving Experience
While it might not rank high on the list of consumers, the analytics that comes along with the use of AI technology in the automotive industry will provide insights into far more than just the safety of the car. It will also manufacturers to tweak and adjust their production line to better serve their customers, whether it’s comfort or access to more accessories. AI insights and analytics will also allow servicing agents to pick up flaws and errors in the car, as well as ways that the driver can reduce wear on the cars. For car owners, knowing how their driving style is impacting their tire wear, fuel consumption, and the overall health of their car can be a game-changer in the way they make the purchasing decision. While this tech will be available in all autonomous cars that come off the production lines, testing these algorithms can start on existing cars in order to gain perspective on driver behavior, desires, and needs.
Safer Driving With Driver Monitoring
Driver monitoring is already taking place as transport owners use apps to manage their fleets and monitor the health and well-being of their drivers. AI uses information such as heart rate, eye movements, and response time to alert drivers and owners of potential danger, such as health conditions or falling asleep behind the wheel. This can also work well for other drivers, who can access their information through the car’s touchscreen with the help of an app. Similar technology is also being explored where drivers will no longer be able to operate a car under the influence of alcohol, thanks to sensors in the interior.
Building Smarter Cars
It’s not just the on-the-road features that make tech like AI and ML a welcoming addition to the automotive industry. Manufacturers that rely on streamlined processes to get the most out of their builds will also benefit from AI and ML, especially when mass production is required to meet the business objectives. The auto manufacturing industry needs to provide spec models that don’t deviate from each other in any way, and with ML, this process is perfected. This means a greater output at maximum efficiency, which can have a positive impact on the bottom line. ML is also an important factor in determining the tech to be used in the car itself, and the most efficient assembly.
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