AI Transportation

Right now, the automotive industry is enjoying a significant level of investment, most of it on artificial intelligence. And more specifically, on optimizing self-drive technology, all aimed at the mass production of level-5 autonomous technology cars.

At the same time, a number of other companies lay claim to the fact that they are playing a leading role in transforming the automotive market. These include Tesla, making improvements to their Autopilot system, Uber, testing out Robo-taxis and Google, running programs on autonomous car development through a subsidiary company called Waymo.

Autonomous cars, trucks, smart containers, self-organizing fleets, smart cities and driver-less taxis – these are all examples of what the future holds for the transportation industry. We already have the first transition to autonomous cars. This being the advanced safety system technology that is already available on some cars, such as auto-braking, alerts to lane deviations, understanding road signs and many more systems that can help keep drivers safe from accidents. In the very near future, technology is expected to bring huge changes, both to vehicles and to the transportation ecosystem.

Very often, using advanced technology in the transport sector can run into difficulty because of unpredictable factors. Traffic, accidents, human error, none of these are predictable. But artificial intelligence has definitely found a place in the transportation industry.

AI makes use of observed data to predict or even make the appropriate decisions. The integration of the technology has resulted in much lower labor costs, a solution to long driving hours, to breaks needed between drives, all with automated fleets. With innovation like this, the future of transportation has already arrived and we expect to see companies rethinking their job descriptions in the future, working out when smart technology can do the job and when human intervention is required.

Plus, with industry-wide standards such as ACC (adaptive cruise control), ADAS (advanced driver assistance systems), and blind-spot alerts emerging, the growth of artificial intelligence is expected to be fueled even further.

Some of the ways artificial intelligence is expected to impact the transportation industry positively (and already does in some cases), are:

Public Safety

AI can help transportation industry companies to ensure public safety when they use their services. For example, real-time crime tracking can help keep citizens safe when they use public transport in urbanized areas. This ensures the police are able to place resources where they are needed, increase efficiency and ensure regular patrols protect the public.

Autonomous Vehicles

Self-drive cars are the talk of the town right now and extensive testing is already underway. These cars use AI to function correctly and make decisions that are fully calculated. Already they are proving that accident numbers will drop and that productivity will be significantly increased.

Better Decision-Making and Planning

The road-freight sector can make good use of highly accurate prediction systems using AI to predict future volume. This makes their planning process simpler. There is also scope for decision-making tools to be designed using AI, which will have a productive and positive effect on investments made by transport companies.

Pedestrian Safety

With artificial intelligence, it is possible to predict the path that cyclists and pedestrians will take, helping to decrease road accidents and injury. This will lead to a more diverse use of transportation and a significant reduction in vehicle emissions.

Controlling Traffic Flow

Traffic flow has always had a significant impact on transport and not always positively. If traffic flow data could be adapted for traffic management models built on AI, traffic patterns could be streamlined and there would a huge reduction in congestion levels. And real-time tracking, together with smart algorithms for traffic lights could effectively control both high and low patterns of traffic. The same technique could be used to optimize the routes and schedules of public transport.