Developers all over the world are pushing the limits on the applications and implementations of Artificial Intelligence and Machine Learning. The automobile industry has also received its fair share of AI/ML applications, automated driving being the biggest example.
Today’s vehicles already incorporate some levels of autonomy. Tesla and Cadillac (with their advanced driver assistance systems) provide Level 2 partial driving automation, while the 2019 Audi A8L has Level 3 autonomy and facilitates conditional driver autonomy. Besides automated driving, the automobile industry is replete with other examples of AI/ML integration across manufacturing processes, system integration, wheel balancing, control architecture, etc., visible from factories to dealership showrooms.
So, what can the average enterprise look forward to from this transformation? How does it affect the decisions to be made by auto industry executives? Should investors embrace this new frontier? Continue reading to understand more about the impact of AI/ML on the automobile industry and anticipate its effect on your business and the industry.
A Quick Look At The Fundamentals Of AI and ML
Artificial Intelligence and Machine Learning are co-dependent technologies. Artificial Intelligence caters to automated problem-solving by mimicking human intelligence. Machine Learning is a dimension of AI focused on developing and perfecting algorithms that can execute independent thinking and reasonable decision-making by human standards.
Speech recognition, neural networks, and recommendation engines are a few examples of AI/ML models.
Data plays a critical role in developing an AI/ML model. A large volume of accurately captured and processed data helps ML algorithms understand an environment, its actors, possible actions, consequences, etc. It helps them establish contexts and learn human behavior bit by bit. Hence, the AI gets “smarter” at recognizing a given subject with time. The ultimate goal is to have an AI that recognizes a variety of subjects in a random scenario similar to human brains, regardless of how obscure the input data may be.
The Role Of Data Annotation
Computers don’t have inherent intelligence, so they must be trained to identify entities. This training happens through annotation. Data (text, image, video) is labeled accurately and then fed to the machine learning model that processes this information.
This process requires large datasets (like images or videos of roads for an automated driving project). Professional audio, video, & text annotation experts add tags to different elements in the data (like, labeling red lights, pedestrians, stop signs, hydrants, etc.), and then the annotated dataset is fed to the algorithm. The AI/ML models get better at identifying the target subjects in random samples when the process is repeated many times.
Text, video, and image annotation are used to develop Computer Vision, where computers identify and comprehend the world around them. Audio annotation is used to develop text-to-speech and other such audio recognition applications.
AI and ML In The Automobile Industry
The automotive industry is a global behemoth with many moving parts (no pun intended). Its value chains stretch across continents, consisting of not just Original Equipment Manufacturers (OEMs) but also parts suppliers and dealers. Every one of those components is set for the AI transformation as the technology increases efficiency while cutting costs and operational times.
Here’s a look at the effects of Artificial Intelligence and Machine Learning on the various components of the automobile industry in-depth.
An automobile goes through various stages during its manufacturing process, starting with concept design, digital model creation, simulation, testing, parts manufacturing, assembly, safety, and quality assessment phases, and so on. AI and ML are playing increasing roles in streamlining these stages.
An AI algorithm can generate any number of car design sketches using a few pertinent parameters. It can facilitate virtual feasibility testing. Robotic means have already been aiding manufacturing factory workers with many activities for a long time now. On a more advanced level, AI/ML models can be used to monitor factory machines and predict maintenance requirements by assessing data on a machine’s noise, vibration, etc. Additionally, AI/ML may also facilitate completely customized vehicle manufacturing, thus increasing driving accessibility to great levels.
Moving manufactured vehicles and parts around the world is a risky, laborious undertaking. There are multiple variables to consider like shipping company demands, weather patterns, insurance, import/export particulars, etc. Inventory management is another paradigm altogether, with unsold vehicles causing major losses. Complete supply chain management and accounting for each of these factors is a complex and difficult process.
AI and ML can remove the bottlenecks at every stage of the supply chain by learning and adapting to the process.
For example, AI is combined with the Internet-of-Things (IoT) to automatically track shipments across the globe. Small electronic circuits with unique IDs are embedded in the supply crates, and the ships carrying them are tagged with GPS technology. AI monitors all relevant data given out by these tags and processes it to give details about the shipment’s condition and location. Data from satellites for weather and traffic can be added to the other data types to give a complete picture of the status of the shipment and predict any issues.
Automated driving (self-driving cars) is perhaps the aspect of automobile automation that the general public is most familiar with. It involves attaching sensors like cameras, LIDAR, RADAR, etc., to gather data about its surroundings and itself. This data is then processed by the onboard AI in real-time to determine how to control the vehicle and transport it to the intended destination.
Vehicles already have some level of automation built into them, with onboard computers assisting drivers with options like cruise control. The level of automation a vehicle has is determined by an Autonomy Level number, with 0 signifying complete manual input and 5 being fully autonomous.
Besides cars, boats and airplanes are adding autonomy to their operation too. Much of a commercial airplane’s journey is done by its autopilot feature, with manual pilot input only being required during take-off/landing phases and emergencies. The use of autonomous drones in warfare is old news now, and companies are experimenting with their commercial counterparts to deliver goods autonomously.
Through data analysis and event prediction. AI/ML models can identify a potential problem well before it occurs and also devise an efficient solution.
Nowhere is this more apparent than on the road. For example, Tesla’s Autopilot self-driving feature is continuously on the lookout for potential collision scenarios. Though not perfect, it has already saved many from deadly accidents by swerving out of the way or slowing the car down. The AI can also call emergency services post-accident so that they can reach the accident spot early and save lives. Its Sentry mode records short clips of the car’s surroundings when parked if it senses any suspicious activity triggered by the input from the car’s cameras. Very sophisticated ML has been used to achieve this as the AI should be able to decide between suspicious and unsuspicious activity. Tesla cars’ self-parking and Smart Summon (remote vehicle transportation) features are added conveniences that also add to the vehicle user’s safety.
AI is also helping keep manufacturing facilities safe. It can predict problems that tend to occur on the factory floor like machinery failure, defective manufacturing, delays, etc. It helps keep product quality high by manufacturing it strictly according to specifications and conducting on-point, consistent quality checks. It can also monitor the condition of the building itself through various industrial IoT devices and highlight potential safety hazards. AI/ML models can also ensure premise safety through systems integrated with advanced facial recognition and behavior monitoring.
Artificial Intelligence and Machine Learning possess safety-enhancing potential. Combined with IoT, they can help maintain equipment, devices, etc.
For example, a vehicle’s AI can run diagnostics to determine its health and indicate the need for associated maintenance. It can also pick up on defects after a collision, determine the severity using data, and may even alert the nearest help center. The maintenance crew can use this diagnostic data to better understand the problem and devise the best solution. The AI may even recommend the solution itself by analyzing the diagnostic data. In manufacturing plants, AI can detect defects in the equipment and issue alerts to the concerned authority. Industrial IoT can help maintain the structure and support systems like Air conditions, water supply, etc.
Training AI & ML systems to conduct safety assessments is the ideal way to prevent catastrophic failures in advance.
Financial and Legal Assistance
Insurance is a major part of the auto industry, covering not just individual vehicle owners but also entire manufacturing facilities and supply chain components.
Insurance companies can use AI to process claims quickly and rationally. They can use the data recorded by a vehicle’s AI system to verify the sequence of events that led to an incident and consequent claim requests. This helps prevent insurance fraud, making the processing more efficient. The auto industry’s associated legal aspects are also improved by using AI. It can process crucial documents easily and quickly, reducing delays and accuracy issues.
Features like Tesla’s Sentry Mode can provide the evidence that legal personnel needs to make a case in favor of victims. It helps the Police by reducing their workload, helping speed-up investigations, and making them more accurate as well. This also applies to the factory and logistics segments.
Consumer demand is pushing the auto industry to new frontiers, such as fully automated driving. The benefits of cost-effectiveness, safety, and efficiency they offer are transformative to all aspects of the industry. The industry, in response, is having to adopt new methodologies and practices to deliver on its promises.
While the significance of AI & ML cannot be ignored anymore, its facilitators (like data recognition, video annotation, labeling, etc.) must also be considered by businesses if they want to lead the wave of transformation. Timely adaptation will aid an enterprise in creating versatile solutions and securing the future of their endeavors in this domain.
Guest article written by: I am Jessica, operations & project manager in Data-Entry-India.com, providing growth solutions, coordinating and leading diverse teams and departments. Passionate blogger, growth hacker for thousands of business owners around the world.