Artificial Intelligence for Automotive Applications – A Close Look at the Revolutionary Trends
The automotive industry is at the cusp of a phenomenal transformation.
Modern cars equipped with AI-powered Advanced Driver Assistance Systems (ADAS) are manoeuvring and finding parking spots themselves. Vehicles are even able to detect when the driver is tired and send alerts to keep them awake and safe!
About a decade or two ago, these features would only have been envisaged in science fiction movies or books. But things are different today.
Automakers have started incorporating advanced technologies in their products and operations to ensure that they stay a step ahead of competitors in the market.
Hence, the modern automobile is increasingly being fortified with IoT technologies like:
- 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
- Applications that use 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.
Automotive OEM-Technology Provider Nexus
The relationship between automotive OEMs and niche software technology solution providers have only strengthened due to these tech 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 technologies and embedded software have been combining business understanding with powerful tools and processes to deliver customised solutions for our clients.
In this article, we explore the application of artificial intelligence in the automotive industry. We also touch upon some AI/ML based solutions we have conceived at our IoT Innovation Lab in Bengaluru.
AI Use Cases in the Automotive Industry
The visionaries in the connected car and autonomous driving arena take advantage of AI and data science for developing disruptive automotive technologies.
Listed below are three far-reaching automotive trends, powered by machine learning and data science.
- Predictive Maintenance
Predictive Maintenance is perhaps one of the finest examples of how data science can be harnessed for adding value to automotive businesses. Predictive maintenance can be leveraged for:
- Manufacturing Analytics and Quality Control
Welcome to the world of collaborative robots working hand-in-hand with humans to boost the efficiency of manufacturing operations!
Analytics has proven to be an extremely powerful tool in the manufacturing value chain. 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 means 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.
Artificial intelligence in car manufacturing helps automakers reduce manufacturing costs and provides a safer and more efficient factory floor.
Cobots (collaborative robots) are emerging as heroes here, as they are increasingly appearing on factory floors to put together the nuts and bolts of the vehicles!
That’s not all, AI-based systems powered by Computer Vision can easily identify product anomalies and reinforce quality control. AI also helps in predicting malfunctions in automotive parts.
ML algorithms can be utilized for prototyping products and simulation.
Why should data science be restricted to only the manufacturing modules? When its application is extended across the entire business, the following divisions can also be benefited:
- Product research and development
- Supply chain
- Sales and customer service
- Vehicle Maintenance Recommendations
Did you know that machine learning in the automotive industry has a far-reaching impact on vehicle owners? ML algorithms can give 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.
A significant use case of predictive maintenance in the automotive industry is in fleet management. Companies that manage fleets of vehicles utilize predictive maintenance to avoid unprecedented repairs and protect the ROI on each vehicle.
Predictive maintenance also enables automotive OEMs to boost brand loyalty. OEMs are increasingly integrating predictive maintenance technology in their vehicles to improve customers’ compliance with vehicle maintenance schedules. This enhances customer satisfaction and boosts brand reputation.
- Manufacturing Analytics and Quality Control
- Driver Behavior Analytics
Driver distraction is one of the leading causes of road accidents globally.
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.
Rash driving – IoT 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, a driver behaviour app developed 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 their driving and take corrective actions to stay safe.
Driver distraction – Machine learning based apps can detect driver distraction and provide early warning signs.
For instance, a driver may 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, or reaching for the infotainment system to play music
These activities are generally classified as driver distraction activities, and they usually take the driver’s attention off the roads.
DriveSafe, a real-time driver distraction detection app incubated at the Embitel Innovation Lab, can analyze driver actions and classify the activities as “focused” or “distracted”. The driver is then notified of distracted driving through audio and SMS alerts, so that they can bring back their focus on the road.
Driver drowsiness – Rash driving and driver distraction are not the only behavioural aspects that can be detected by ML-based apps. These apps can also detect eye openness and head position of a driver. If the driver is found to be drowsy, the app sends a notification to alert them.
Heard of Pay As You Drive (PAYD) or Usage Based Insurance? This is a new form of auto insurance that determines your premium based on factors like how many miles you drive or how safe you are on the roads. This type of insurance uses telematics technology to monitor the driver and the vehicle.
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 their 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.
- 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.
If a person is driving in a new city, they would have to completely depend on a web mapping service for best route suggestion. They 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.
Artificial Intelligence in Autonomous Vehicles
Autonomous vehicles view, perceive and make decisions in split seconds. AI, ML and computer vision technologies facilitate in assessing the outside world. In addition, edge and cloud computing applications work in tandem to convert massive volumes of vehicle data into real-time actionable insights.
Trends in Autonomous Vehicle Tech
- Deep Learning - Modern computer vision systems use deep learning to achieve robustness and superior performance. Large neural networks with multiple hidden layers are leveraged for feature generation, training and prediction. Deep learning is widely used for automating automobile manufacturing assembly tasks. Visual inspection activities such as part identification, part selection, defect detection, etc. can be automated to improve product and process quality.
- Edge AI - Another trend that has gained grounds in autonomous driving tech is the use of Edge AI applications. It is essential for autonomous vehicle subsystems to have extremely low latency. These systems should also be able to function if there is a temporary loss of internet connection. Edge AI applications are perfect for such use cases. Hence, autonomous vehicles are connected to the cloud, but most of the data processing activities happen locally.
Applications of Machine Learning in Autonomous Vehicles
Machine learning and computer vision technologies can process a large amount of data from camera sensors. This information is then combined with data from other sensors such as Lidar, in a process referred to as Sensor Fusion. Subsequently, this data can be used to generate the following insights:
- Real-time prediction of collision
- Driver distraction
- Real-time detection of road conditions and road signs
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!