Nowadays, there are numerous investments in the automotive industry. The majority of these investments are focused on artificial intelligence (AI) and self-driving technology optimization. Meanwhile, new mobility systems and players are making their way into the automotive market. Tesla is attempting to improve its autopilot system, Uber is experimenting with Robo-taxis, and Google is working on self-driving cars.
How is the Mobility Landscape Changing?
Mobility technology is a driving force in making transportation more user-friendly and accessible. Smart mobility is propelling city economic growth, improving logistics supply chains, increasing labor market accessibility, and opening up new markets for businesses.
Smart mobility technologies such as shared bikes and e-scooters are changing the urban mobility ecosystem, and cities are responding by building dedicated lanes and improved transportation systems.
Why is AI Important to Mobility?
Smart cities all over the world strive to provide more efficient and environmentally friendly transportation options. Transportation options such as Connected Autonomous Vehicles (CAV) and drone deliveries will soon dominate the mobility landscape. Because of the constant increase in traffic, traffic-induced noise and pollution, and the limited availability of space in urban areas, the way people and goods move will have to change dramatically.
Machine learning, artificial intelligence, and data analytics are used by organizations to identify, predict, and solve mobility challenges. Companies and cities can use artificial intelligence technology to transition to autonomous mobility — highly personalized and environmentally 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 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 a greater social experience when riding with people of similar interests. This can change traditional transportation networks and transform the way people commute.
3. Driver Tracking
A driving monitoring system uses cameras to monitor the alertness of the driver. The system detects the driver and assesses his or her level of concentration. When a driver is distracted, the system alerts them. Drivers can use AI-based systems to customize vehicle settings such as seat position, temperature, and mirrors.
This technology is based on AI algorithms that monitor head position, eye openness, and other alertness indicators. If necessary, the system alerts the driver to refocus or take a break. Posture management allows for the most effective deployment of airbags in the event of an accident.
4.Self-Driving Vehicles
Despite public skepticism, driverless vehicles are slowly but steadily making their way into the transportation sector. While the majority of self-driving vehicle companies are still running pilot projects to ensure passenger safety, some have already deployed vehicles on public roads. As this technology advances, self-driving vehicles may gain widespread acceptance and become the norm 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
Every day, people face traffic congestion issues. AI has matured to the point that it can now solve this problem. Cameras and sensors are embedded throughout roads to collect traffic data. These details are sent to cloud-based big data analytics and AI-based systems. These big data tools perform an analysis to identify traffic patterns and predict congestion.
Drivers can also use AI to improve road safety and reduce wait times. Important traffic information such as accidents, road closures, and the shortest routes to a destination can assist drivers in traveling without encountering 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 to improve car assembly lines as well as to protect factory workers.
Another example is Automated Guided Vehicles (AVGs), which are designed to transport materials without the use of humans. Welding and painting robots powered by AI can detect manufacturing flaws and adjust their processes accordingly. These AI-powered robots make automobile manufacturing safer, more efficient, and less expensive.
7.Insurtech
Technology applications in insurance, also known as insurtech apps enable drivers to file their 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 of cities and use self-driving cars 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 ever-changing. Artificial intelligence and machine learning are already reshaping the automotive industry. AI-based systems aid in the reduction of traffic accidents and fatalities, the reduction of manufacturing costs, and the improvement of service levels. On the other hand, when businesses fail to adapt and follow the new trend, AI-based automation and systems can result in technological unemployment.
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