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There are a significant number of investments in the automotive industry nowadays. The majority of these investments focus on artificial intelligence (AI) and the optimization of self-driving technology. Meanwhile, new mobility systems and players are making their way into the automotive market. Tesla is trying to improve its autopilot system, Uber is testing robo-taxis, and Google is developing self-driving cars.
How is the Mobility Landscape Changing?
Mobility technology is a driving force behind user-friendly and accessible transportation. Smart mobility is driving the economic growth of cities, enhancing logistics supply chains, improving labor market accessibility, and opening new markets for businesses.
Smart mobility is changing the mobility ecosystem in urban environments with technologies like shared bikes and e-scooters, and cities are building dedicated lanes and improved transportation systems in response.
Why is AI Important to Mobility?
Smart cities around the world strive to provide more efficient and greener transportation options. Transportation options like Connected Autonomous Vehicles (CAV) and drone deliveries are the next technologies to dominate the mobility landscape.
The way people and goods move will have to change dramatically due to the constantly increasing traffic, traffic-induced noise and pollution, and limited availability of space in urban areas.
Organizations use technologies like machine learning, artificial intelligence, and data analytics to identify, predict, and solve mobility challenges. Artificial intelligence technology enables companies and cities to transition to autonomous mobility — highly individualized and environmental-friendly systems.
Eight Ways AI is Impacting Mobility
Here are the top eight ways in which AI is changing mobility.
1. Smart Grid Management
Electric cars need to charge their batteries every now and then. But many people don’t know that they can also resell the power stored in electric cars’ batteries back to the electrical grid. AI can predict the best times to charge your electric vehicle and when to use it for Vehicle-to-Grid (V2G) “un-charging.” Smart grid management enables drivers to reduce costs and increases the efficiency and stability of the entire grid.
2. Transportation Systems
Mobility-as-a-Service (MaaS) enables users to quickly plan trips using different means of transportation. With MaaS, commuters can book, manage, and pay for rides using personal devices like smartphones or other connected devices.
When powered by AI, MaaS systems gain significant benefits, like fully autonomous driving and smart tracking. Additionally, MaaS with AI-based controllers can optimize, monitor, and coordinate autonomous car fleets while offering great options to individual users.
AI-based MaaS can also be applied to ride-sharing, enabling users to share autonomous cars across an optimized route in a much cheaper and safe way. Ride-sharing users can also get greater social experience when riding with people of similar interests. This can change traditional transportation networks and transform the way people commute.
3. Driver Monitoring
A driving monitoring system uses cameras to monitor driver alertness. The system recognizes the driver and checks his or her level of attention. The system alerts drivers when they are distracted. Drivers can use AI-based systems for adjusting vehicle settings such as seat position, temperature, and mirrors to their preferences.
This technology is based on AI algorithms that measure head position, eye openness, and other indicators of alertness. The system warns the driver to regain focus or take a break if necessary. Posture management enables the best possible deployment of airbags in case of an accident.
4. Self-Driving Vehicles
Driverless vehicles are slowly but surely making their way into the transportation sector, despite public skepticism. While the majority of self-driving vehicle companies are still running their pilot projects, trying to make them safe for passengers, some companies have already deployed vehicles to public roads. In the future, as this technology evolves, self-driving vehicles can gain mass confidence and become mainstream for consumers.
Computer vision and deep learning systems are the brains of a self-driving car. These systems are responsible for processing and giving context to all the information that comes from the sensors. A self-driving car’s data is coming from multiple different sources like cameras, radar, and Light Detection and Ranging (LiDAR). Computer vision systems need to process data from all these different sources. As a result, environment information gathering becomes a very comprehensive task.
5. Traffic Management
People face traffic congestion issues on a daily basis. AI has matured to the point that it can now solve this problem. Cameras and sensors are embedded everywhere on roads to gather traffic details. These details are sent to big data analytics and AI-based systems in the cloud. These big data tools perform an analysis to identify traffic patterns and predict congestions.
Drivers can also use AI to improve road safety and reduce wait times. Important traffic information like accidents, road blockages, and shortest routes to a destination can help drivers travel without any traffic congestion.
6. Manufacturing
AI is also changing the way cars are built. For example, Kia recently introduced the Hyundai Vest Exoskeleton (H-VEX) wearable robot. The robot is used both for improving car assembly lines and protecting factory workers.
Another example is Automated Guided Vehicles (AVGs), which are designed to transfer materials without human intervention. AI-based welding and painting robots can identify manufacturing defects and change their processes accordingly. These AI-based robots make car manufacturing safer, more efficient, and cheaper.
7. Insurtech
Technology applications in insurance, also known as insurtech, are based on the deep learning capabilities of AI. This includes DIY insurance apps that enable drivers to file their own damage assessments after an accident and driver risk assessments based on risk factors filtered out of big data.
8. Smart Cities
AI-based systems have a huge impact on the way cities expand and grow. For instance, autonomous vehicles can initiate a de-urbanization trend. Autonomous vehicles offer a cheaper, faster, and safer way to commute. People can live outside the city and use autonomous vehicles to get to work quickly. And since they aren’t driving, they can be fully productive during the ride, using commute time to continue working while the AI drives the vehicle
Conclusion
The transportation industry is constantly changing. New technologies like AI and machine learning are already dominating the automotive industry. AI-based systems help in reducing road accidents and fatalities, lower manufacturing costs, and increase service level. On the other hand, AI-based automation and systems can cause technological unemployment when companies fail to adapt and follow the new trend.