Artificial Intelligence for Automotive Applications – A Close Look at the Revolutionary Trends

Artificial Intelligence for Automotive Applications – A Close Look at the Revolutionary Trends

Today, the automotive industry is at the cusp of a phenomenal transformation. Ambitious automakers have started incorporating advanced technologies in their products and operations to ensure that they stay a step ahead of competitors in the market.

The modern automobile is fortified with IoT technologies and applications:

  • Sensors that gather valuable data about vehicle condition and driver behavior
  • Complex machine learning (ML) algorithms that convert the collected data to insightful reports, and
  • Usage of this data to segment customers and provide individualized offers

These are some of the most prevalent use cases of artificial intelligence for automotive applications today.

EV automotive OEMs

The relationship between automotive OEMs and niche software technology solution providers have only strengthened due to these advancements.

As a trusted technology partner for global automotive OEMs and Tier 1 Suppliers, Embitel has been developing transformative solutions for connected cars of the future. Our IoT team consisting of experts in artificial intelligence (AI), cloud solutions and embedded software have been combining business understanding with powerful tools and processes to deliver insights for future decision making.

In this article, we take a look at some of the distinct AI/ML trends in the automotive industry and the associated ideas/products we have conceived at our IoT Innovation Lab in Bengaluru.

AI Use Cases in the Automotive Industry

Implementation of artificial intelligence and data science has benefited not only the car manufacturers, but also parts/software suppliers, vehicle rental companies and other businesses related to the automotive domain.

The visionaries in the connected car and autonomous driving arena take advantage of data science and AI for developing disruptive technologies in the industry.

Listed below are three far-reaching automotive trends, powered by machine learning and data science.

  1. Predictive Maintenance

    Predictive Maintenance is perhaps one of the finest examples of how data science can be harnessed for adding value to automotive businesses.

    • Manufacturing Analytics – Analytics has proven to be an extremely powerful tool in the manufacturing value chain. In order to realize the full potential of data science, it is important to analyze and collect data from various functions across the manufacturing life cycle.

      This indicates that an end-to-end analytics strategy that covers workforce analytics, asset/inventory management and operational planning is crucial for getting the complete picture for generating insights.

      The use of AI in vehicle manufacturing helps automakers reduce manufacturing costs and provides a safer and more efficient factory floor.

      Technologies such as Computer Vision enables easy identification of product anomalies. ML algorithms can be utilized for prototyping products and simulation.

      AI also helps in predicting malfunctions in automotive parts. This helps the manufacturing systems to work at prime performance levels, and save time and money in the long run.

      Manufacturing Analytics

      Apart from the manufacturing module, the use of data science can be extended across the entire business to benefit the following divisions:

      • Product research and development
      • Procurement
      • Supply chain
      • Distribution
      • Sales and customer service
      • Aftermarket
      • Marketing
    • Vehicle Maintenance Recommendations – Machine learning algorithms can be employed to provide evolving recommendations to drivers about vehicle maintenance. Based on the past occurrence of an event/issue, it is possible to predict when the next such issue may happen.

      So, for instance, the data collected by a vehicle’s sensors may indicate gradual overheating, friction or noise. These issues may also lead to the breakdown of a specific vehicle part in the future.

      • The machine learning algorithm records these events regularly and analyses the frequency of occurrence of these issues.
      • It also accurately predicts when the breakdown of the vehicle or part is expected, based on the findings.
      • The driver can, hence, take precautionary measures by getting the vehicle inspected and maintenance activities scheduled to avoid such a breakdown. This is a classic example of predictive maintenance in automobiles.

       

      Vehicle Maintenance

      Fleet management companies also utilize predictive maintenance to avoid unprecedented repairs and protect the ROI on each vehicle.

      Automotive OEMs are increasingly integrating predictive maintenance in their vehicles to improve customers’ compliance with vehicle maintenance schedules, enhance customer satisfaction and boost brand reputation.

      Over the years, our engineering teams have been working with global OEMs to develop software that effectively predicts the maintenance requirements of vehicle parts.

  2. Driver Behavior Analytics

    AI and Deep Learning based automotive applications can offer a plethora of valuable in-car analytics. Cameras and IR sensors can detect the driver’s behavior accurately and provide warning signals to avoid accidents. Some of the key focus areas of driver behavior analytics include detection of:

    • Rash drivingIoT sensors can collect data on driver speeding, sharp turns, sudden braking, etc. This information can be analyzed continuously to form an impression of the driver’s behavior on the road.

      Project Genie, an intuitive mobile app developed by the engineers at Embitel, can evaluate the driver’s road performance and provide feedback at the completion of each journey. This helps the driver understand the issues with his driving and take corrective actions to stay safe.

    • Driver distraction – Machine learning based automotive applications can assist drivers by detecting driver distraction and providing early warning signs.

      For instance, a driver may also be engaged in several other activities while driving. This includes attending calls on a mobile phone, texting, reaching out to the back seat, talking to passengers, smoking, reaching for the infotainment system to play music, etc.

      These activities are generally classified as driver distraction activities and they usually take the driver’s attention off the roads.

      Driver Distraction Detection App

      DriveSafe, a real-time driver distraction detection app incubated at the Embitel Innovation Lab, can analyze driver actions and classify these activities as “focused” or “distracted”. The driver is then notified of distracted driving through audio and SMS alerts, so that he/she can bring back his/her focus on the road.

    • Driver drowsiness – Machine learning based automotive apps enable the detection of eye openness and head position of a driver. The app subsequently sends a notification to alert the driver, if he/she is found to be drowsy.
  3. Driver behavior analytics in the insurance industry:

    The insurance industry can leverage driver behavior analytics to determine car insurance premiums for customers.

    Insightful risk profiles are created for each driver based on his/her performance on the road, issues in personal life, health complications, and a myriad of other such factors that could affect their driving. This information is the basis for determining the premium.

    The process of filing insurance claims at the time of an accident can also be streamlined through the use of AI.

  4. Analyzing Road Conditions

    AI-powered automotive applications can detect road conditions in real-time so that drivers can be updated of construction work, accidents, speed limits and road closures before they start the journey.

    The AI/ML engineers at Embitel have conceived an IoT-based smartphone app to analyze road conditions and provide appropriate navigation assistance to drivers based on these conditions. This app determines the most optimum path for the journey based on potholes, humps, and road closures. The driver is also warned of the approaching hump/pothole, around 100 meters before he/she reaches it.

    In cities where there is frequent traffic congestion and road construction work, this information is very valuable for commuters.

    Analyzing Road Conditions

    If a person is driving in a new city, he/she would have to completely depend on a web mapping service for best route suggestion. He/she would not know about the untarred roads that may be part of the optimized path suggested by the app. The smartphone app developed by our engineers can combine road condition info and traffic details to provide the best path suggestion.

Conclusion

The automotive market is seeing increased competition, cost pressure and volatility. Even small interventions can help auto OEMs make great strides in increasing their market share. Since data science is emerging as a game-changer in the automotive industry, the opportunities offered by it are plenty.

The perfect time to adopt artificial intelligence for automotive applications is now. If you have an idea for an exciting AI/ML based application, reach out to us. We can help you in transforming your vision to reality!

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