AI in Mobility

Artificial intelligence is having a huge impact on the transport sector. It is playing its role in enabling cars to function autonomously, and making traffic flows smoother. Artificial intelligence-led autonomous vehicles reduce the human errors that are involved in many traffic accidents, hence can make all transport modes safer, cleaner, smarter and more efficient. Along with many opportunities they also have some challenges, like cyber security issues and policy development which are also being tapped by different companies to come up with innovative products. The governments across the world are taking steps to adapt its regulatory framework to these developments. According to a report by PSI, the global AI in transportation market is projected to reach $3.5 billion by 2023.

Autonomous Transport

Autonomous Transport

Innovation within the automotive sector has created safer, cleaner, and more affordable vehicles, and has brought the industry close to substantial change. As autonomous-driving technology is advancing, new transportation use cases are emerging, that are driving new business models, value chains, and strategic decisions. AI is crucial for realizing autonomous driving as they provide extreme compute performance required. Advanced Driver Assistance Systems (ADAS), vision systems, radar-based detection units, driver condition evaluation and sensor fusion engine control units (ECUs) are also some of the areas where AI is playing its role or has a potential.

  • Q.Self driving cars

    Autonomous cars need to have the ability to see, think, learn and navigate a nearly infinite range of driving scenarios, for which Artificial Intelligence is needed. There are companies that are harnessing the power of AI and deep learning for delivering breakthrough end-to-end solution for autonomous driving. AI is utilized for data collection, model training, and testing in simulation to the deployment of smart, safe, self-driving cars.

  • Q.Vision systems

    : Autonomous Vehicles are dependent on AI and machine-learning strategies. AI dependent sophisticated embedded-vision systems capture large volumes of scene data that are processed to plan vehicle route. AI is leveraged to process and manage large amount of data from multiple sensors while working with the heterogenous multiprocessor system architectures. The ability of AI algorithms to evolve rising complexity is also playing an important role. It is also being used to correct deficiencies in individual sensors, and to enable more accurate position and orientation tracking.

  • Q.Driving assistant

    ADAS technology is used for applications like blind spot monitoring, lane-keep assistance and forward collision warning. AI platform takes driver assistance to the next level by employing deep learning It provides the developers with powerful foundation for building applications for object detection, map localization, path planning etc.


Auto industry is now not about merely moving us from point A to point B, but changing the way the world moves. Connectivity is at the heart of all of this, driving a huge number of innovations that are either here now or in the works. The services like letting driver to make hands-free phone calls, control the entertainment, climate and navigation systems using voice are emerging. These technologies lets to remotely start vehicle, unlock the doors, check the fuel level, and much more from smartphone. For services and products like Infotainment human-machine interface, including speech recognition and gesture recognition, eye tracking and driver monitoring, virtual assistance and natural language interfaces, AI is central.

  • Q.Vehicle Cybersecurity

    The autonomous vehicles produce more data than any human capacity can possibly handle and this also adds to the vulnerability of the vehicles that are constantly connected. As cyberattacks grow in volume and complexity, AI and ML technologies play a significant role. AI can be leveraged to identify potential attacks by conducting behavioral analysis of the data. AI can provide OEMs and smart mobility services providers with data visibility that can ensure protection of their vehicles.

  • Q.In Car Voice Assistant:

    To improve driver experiences in their vehicles, automakers are working on sophisticated voice-enabled infotainment systems to customers access a wide range of services while controlling their in-car environments. These AI enabled Voice assistants allow consumers to interact with the car and also to purchase and pay for utilities without having to take their eyes off the road. The experts predict that these systems will take over with unparalleled speed.

  • Q.Predictive maintenance

    AI translates that data into meaningful insights and data points, circumventing data overload. It can aggregate and analyze huge amount of data from multiple sources like sensors, historical maintenance records, and weather data to detect problems before they occur. It plays a role in keeping the vehicle in operation and preventing its sudden failures thus maximizes efficiency.

Shared Mobility

Shared transportation has grown tremendously in recent years as a renewed interest in urbanism and growing environmental, energy, and economic concerns have intensified the need for sustainable alternatives. Simultaneously, advances in electronic and wireless technologies have made sharing assets easier and more efficient.

Automakers, rental car companies, venture-backed startups, and city-sponsored programs have sprung up with new solutions ranging from large physical networks to mobile applications designed to alter routes, fill empty seats, and combine fare media with real-time arrival and departure information. AI platforms are used in this area to provide all the technology required for mobility service, including advanced fleet management tools, consumer-facing mobile applications and Mobility-as-a-Service (MaaS) operations.

AI is used in mobility-as-a-service models to predict customer demand, optimize fleet efficiency and minimize customer wait times. It also plays its role in dynamically set prices in response to demand and ensure passenger physical security


Electromobility in its many different forms will be an essential component of future mobility. The companies are developing existing concepts and on new developments for climate-friendly hybrid and electric vehicles. In order to shape safe, efficient, connected and comfortable mobility, companies are pooling expertise from all divisions in the fields of integration, energy optimization, drivetrain management, vehicle safety, information management and tires. AI plays its role to predict the best times to charge EV, and to use it for vehicle-to-grid (V2G) supply. AI is also being implemented to advance battery technology creating smarter batteries and battery-management systems.

  • Q.Vehicle-to-grid technology

    V2G technology draws unused power from the vehicle into the smart grid and helps the energy grid supply electricity during peak hours. It can also serve as an extra power source when weather-dependent renewable energy sources are unavailable. Artificial Intelligence and Big-Data Analytics can play an important role in the optimization process.

  • Q.Battery Management Systems

    Lithium Ion batteries used in Electric vehicles present the need of BMS. Here AI analytics can make these systems adaptive, smart, and agile, hence reduce operations, inefficiencies, and lower costs.

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