Digital transformation doesn’t show any signs of slowing down in transforming various industries – and the automotive sector is no exception. With the skyrocketing amount of data in software-imbued cars, the industry has gained significant benefits by implementing machine learning into its product development processes. Big automakers are investing in proof-of-concept projects at all stages.
More and more vehicles are being equipped with technologies to assist human drivers, and cars are increasingly connected, autonomous, and electric. Investing in the right technologies is crucial for customer satisfaction, employee productivity, safety, and collaboration. All this is greatly facilitated by data-fueled applications of Artificial Intelligence and Machine Learning in automotive innovation.
The role of Advanced Driver Assistance Systems (ADAS) is to prevent injuries and deaths by reducing the number of car accidents, of which 90% are caused by human error. According to Together for Safer Roads, ADAS has already reduced the number of bodily injuries by 27% and the number of vehicle damage by 19%.
With ADAS, driving becomes more predictable. Every driver has a unique style of driving – Advanced Driver Assistance Systems sort of taking that away by standardizing on a safer approach to driving. ADAS can react faster, and provide better solutions faster than humans are capable of doing so. ADAS can also take care of blind spots, pulling the emergency brake when necessary.
These are some of the factors why the global market of ADAS is expected to grow from $27.2 billion in 2021 to $74.9 billion in 2030 according to MarketsandMarkets.
Some companies including Tesla, Mercedes, and BMW are testing and building vehicles with automated driving systems (ADS). While real self-driving is a long way down the road, we like to refer to these as autonomous vehicles or self-driving cars – which really is true in certain situations. Vehicles equipped with ADS are already able to handle maneuvers like parking in full autonomy and should be able to operate entirely without a human driver in the future.
Self-driving cars rely on three major types of sensors, namely cameras, lidar, and radar systems. work together just like our brain, eyes, and ears do to perceive the environment. are machine learning algorithms trained on real-life data sets. A car with machine learning has two types of learning models: supervised and unsupervised. The unsupervised learning model means that the algorithm receives unlabeled data and has no instructions on how to process it, so it needs to figure it out on its own. With the supervised learning model, the algorithm gets fed instructions on how to label the data. For cars with ADS, supervised learning is the preferred way to go – that said, this approach is not without its own problems.
Supply chain optimization is a challenge for any type of industry, with the automotive industry being no exception due to the mounting complexity of both its end products and the manufacturing processes it relies on. It makes the best use of technology and resources like blockchain, AI, and IoT to improve efficiency and performance in a supply network. Machine learning (ML) allows the software to learn and adjust without it being explicitly programmed to do so.
Nowadays, supply chains generate a vast amount of complex data. ML can analyze this information and use the information collected to optimize supply chain management. It can also analyze timings and handovers as products move through the supply chain. After analyzing the collected data, it can identify potential bottlenecks and suggest solutions to optimize the supply chain. Using machine learning, organizations in the supply chain don’t need to rely on inventory as much, since ML helps optimize the flow of the product from one point to another. This greatly supports manufacturing efficiency, regardless of whether the carmaker decides to keep inventory due to other reasons (such as shortages, which we’ve seen a lot of in recent years).
Machine learning can help protect the safety of both drivers and vehicles by providing precise and up-to-date maintenance recommendations. A vehicle with predictive maintenance collects data throughout the day, and through alerts based on irregularities, data analytics can predict when the vehicle is in a need of maintenance, as a result avoiding breakdowns and costly repairs.
Because of this, it’s more up-to-date than vehicles that are on a static maintenance schedule. According to Deloitte predictive maintenance increases equipment uptime by 10-20% and reduces overall maintenance costs by 5-10% and maintenance planning time by 20-50%.
Quality is one of the top priorities in the automotive industry. Enforcing quality control throughout the assembly line is a must – and machine learning helps do this at optimal costs. The use of ML can help drive down costs through quality improvement and waste reduction. Machine learning can learn which aspects are fundamental and create rules that determine the combination of features that define quality products. It can also understand the implicit relationship between large data sets in complex and dynamic environments. The quality of every part can be a crucial factor in a critical life and death situation. With the use of sensor-based artificial intelligence, defective objects are immediately removed from the production line. The use of AI can help reduce the percentage of faulty products released, meaning safer cars on our roads.
Hanna Taller is a content creator for PTC’s ALM Marketing team. She is responsible for increasing brand awareness and driving thought leadership for Codebeamer. Hanna is passionate about creating insightful content centered around ALM, life sciences, automotive technology, and avionics.