How AI Is Paving the Way for Autonomous Transportation?

Research into artificial intelligence (AI) has experienced a surge in the last few decades. This was built largely on pioneering results from the 60’s and 70’s, including the utilization of advanced neural networks (NNs). It has even taken inspiration from biological behavior for methods like fuzzy logic or genetic algorithms (GAs).

Autonomous cars have been recently hitting the headlines and dominating tech-talks. It’s seen as a post-Uber disruption to public commuting and transportation of goods. It is surely not a figment of imagination in the age of artificial intelligence (AI) which is being used to complement driverless cars. The combined might of AI and driverless technologies is a formidable force.

The likes of Waymo, Tesla, etc. are heavily invested in driverless cars. Waymo has been testing the driverless car in Phoenix. Tesla has already implemented a couple of “autopiloting” features in its cars.

But before we get into further details, let us discuss what autonomous transportation means.

Self-driving vehicles are cars or trucks in which human drivers are never required to take control to safely operate the vehicle. Also known as autonomous or “driverless” cars, they combine sensors and software to control, navigate, and drive the vehicle.

How AI can help transportation:

Transportation problems arise when system behavior is too difficult to model according to a predictable pattern, affected by things like traffic, human errors, or accidents. In such cases, the unpredictability can be aided by AI.

AI uses observed data to make or even predict decisions appropriately. NNs and GAs are perfect AI methods to deal with these types of unpredictability. AI has been in development and implemented in a variety of ways. Some examples are given below:

• Improvement of Public Safety: Safety of citizens when traveling by public transport in urban areas is improved by tracking crime data in real-time. This will also enable the police to increase their efficiency by patrolling and keeping their citizens safe.
• Corporate Decision Making: The road freight transport system can utilize accurate prediction methods to forecast its volume using AI methods, which simplifies transportation company planning. Additionally, several decision-making tools for transport can be designed and run by AI. This will affect investments made by companies in the future in a productive way.
• Autonomous Vehicles: Self-driven cars and trucks have been of high interest in the last several years. In the commercial sector, Uber and Elon Musk have produced self-driving trucks to reduce the number of accidents on highways and increase productivity.
• Traffic Patterns: Transport is greatly affected by traffic flow. Traffic congestion in the US costs around $50 billion per year. If this data is adapted for traffic management via AI, it will allow streamlined traffic patterns and a significant reduction in congestion. Several similar systems are already in place. For example, smarter traffic light algorithms and real-time tracking can control higher and lower traffic patterns effectively. This can also be applied to public transport for optimal scheduling and routing.
• Pedestrian Safety: The use of AI to predict the paths of pedestrians and cyclists will decrease traffic accidents and injuries allowing for more diverse transportation usage and an overall reduction in emissions.

The impact of AI in transport:

Some Advantage:

Costs of labor in this sector will continually decrease with the increased use of AI, providing higher profits for industry players. The issue of long driving hours and stopping for a break will no longer be a concern with fully automated fleets.

Beyond straightforward labor costs, safety and traffic accidents will be majorly affected by AI. The number of accidents involving truck drivers at night is a large issue and can be significantly improved with the use of smart unmanned vehicles. The personnel and financial costs of these accidents are quite substantial. Auto-pilot or complete unmanned vehicles can allow the driver to have a snooze without causing severe accidents. Some AI trucks even have a special feature of predicting accidents as well as health issues of people around the truck like detecting a heart attack and alerting the emergency services automatically with the location and details of diagnosis.

Some Disadvantage:

Automated trucking has sparked a hot debate among 4.5 million truck drivers in the US alone. Developments would mean autonomous trucks, ships, aircraft or trains slated for the future, along with any future vehicles becoming completely unmanned. Job flow is thus a major concern for truck drivers, taxi drivers, and other members of the industry. Social experts have argued that job skills can be shifted or evolved into other sectors, but tensions remain high.

Implementation around the world presents another major issue. Undeveloped and third world countries face enormous challenges in utilizing these solutions, as their infrastructure is not as stable or capable of providing maintenance and repairs. It will be a long time before AI can become a reality there.

Increasing focus on AI also presents a dilemma for transport companies: transport costs contribute to the company turnover by 3–10%. This makes it a very important factor in corporate economies as a whole. All existing businesses will need to engage in, develop, and implement AI technologies to remain a competitor in the transportation industry. This affects transportation logistics as well, as it is used in the supply chain of operations and manufacturing and even predicting the time and total cost of the entire process.

The future:

By 2020–2022, it is estimated that there will be 10 million self-driving vehicles and more than 300 million smart cars on the road. Tesla, BMW, and Mercedes have already launched their autonomous cars, and they have proven to be very successful.