We are just scratching the service in uncovering the vast potential of Artificial Intelligence (AI) in the automotive sector. Generative AI, Data Science, Machine Learning, Artificial Neural Networks, Text Mining – all these technologies, already partially mature in the online marketing and financial worlds, have much to offer manufacturing in general, and the automotive industry in particular.
From the design and development phase through to testing and production to after sales or marketing, AI has applications throughout the automotive life cycle. The data generated by embedded vehicle sensors, extracted from production lines, or compiled from customer feedback are powerful sources of information. Their analysis and interpretation provide equally powerful levers for improvement in design, testing and maintenance; as well as for understanding user needs and expectations.
Capitalising on customer knowledge
If there is one area in which the effects of big data are particularly well known, it is end-user customer knowledge. Consumer data analysis applications are among some of the most mature and are used by brands to identify their target audiences and the expectations of those audiences. This approach is a direct response to increasing demand for product and service personalisation. In the automotive industry, customer knowledge can be applied to improve customer satisfaction. Example? We have supported a customer with an
AI-based solution, employing Natural Language Processing (NLP) techniques to analyse unstructured customer feedback from various sources such as online reviews, surveys, and social media. The system performs sentiment analysis to classify the reviews into positive, negative, or neutral categories and pinpoints key topics, themes, and specific references concerning automotive products or services. This provides deep insights into customer sentiment, but it also enables the company to address issues, enhance product development, and improve customer satisfaction.
Algorithms for better fault prevention and correction
The multiplicity of data available during the testing phase provides access to information that can prove extremely valuable for fault resolution. All you need to do is be able to extract the data. By detecting faults within large volumes of data, algorithms leave engineers free to focus on data interpretation and fault resolution, rather than searching for source information. This means that methods known as clustering and classification can be used during road testing to analyse and qualify vehicle responses. Using data gathered by in-vehicle sensors, it becomes possible, for example, to identify untimely braking scenarios, understand their causes and ultimately correct them. Without the algorithms, it would be significantly more complex to make use of the data. So, it is clear that AI does not remove the need for people. On the contrary, it refocuses their expertise.
New functions for automated driving & parking
However, the research into the development of smart vehicle technology is focused particularly on the issue of environmental perception: infrastructures, other vehicles, pedestrians or any other object that could be considered as an obstacle to a car. Radar, sensors, cameras, weather conditions, roadworks and other extraordinary events: the machine must be able to recognise every type of external influence and evaluate its possible effect on the trajectory of the vehicle to make appropriate corrections to the driving control system in real time.
Automated Valet Parking (AVP) for example combines the power of AI, data fusion and computer vision and allows the vehicle to navigate coordinated and secure within a controlled environment like a car park. Expleo is working on this issue also through an in-house R&D project within that a full-scale demo car for an end-to-end service around AVP has been developed. As technology is advancing rapidly, we are using this platform also to integrate new applications, such as Predictive Situation Analysis. With data, statistics, modelling, and machine learning this feature is going to detect potentially hazardous situations before they arise.
What about the future of AI for automated driving?
Looking further ahead, the challenge – as tricky as it is inspiring – is naturally the development of autonomous vehicles and the full delegation of all safety-related decisions to the vehicle itself.
One of the major challenges will be validating the safety-related decisions taken by autonomous vehicles. Currently, the on-road use of any vehicle is subject to its ability to demonstrate that it complies fully with a series of predefined safety standards. But in the context of autonomous vehicles, safety will be ensured by AI. Although artificial neural networks are currently delivering promising results, like the capability to respond adequately in emergency braking situations, these results can neither be demonstrated nor guaranteed. So, do we need to evolve the current safety demonstration standards as a result? That’s a question that remains to be answered.
AI speeds up processing time
Visual inspection is another vital component of automotive manufacturing, ensuring each vehicle conforms to customer order requirements Traditionally, a human inspector would require several minutes to conduct these checks, but a camera-based system can accomplish the task in just a few seconds. We used AI to improve the system of an automotive OEM which made it faster and more efficient. Our strategy reduced processing time from 50 to 30 seconds. As the model continues to identify new non-conformities, we are progressing towards an autonomous system where the AI model self-trains, eliminating the need for ongoing intervention from data scientists and further enhancing efficiency.
AI and mechanical design: a developing symbiosis
Despite its very many recent successes in traditional applications, such as image, speech and natural text processing, AI has yet to enter the field of Mechanical Design or Digital Simulation in a more general sense. The reasons lie simultaneously in their tools – data versus physical equations – and the expertise of designers and mechanical engineers, which is challenging to interpret mathematically. Nevertheless, there are many applications for AI, and especially Machine Learning, in mechanical design; examples include building low-cost approximations of design process calculations quickly.
Issue detection techniques based on Machine Learning are now used to identify damage and inspect structures. Used in conjunction with Machine Learning, global optimisation makes it possible to design better structures more quickly than the traditional trial-and-error approach by exploring a more expansive design space. Beyond these known applications, AI will release the creative potential of mechanical engineers by assisting with, and facilitating, certain time-intensive tasks, such as the construction or reconstruction of parametric CAD models. Generative Adversarial Networks will automatically generate increasingly realistic and powerful 3D designs.
Lastly, available storage capacity and the distributed processing of big data will make it possible to chain the many calculations required to design a component. We are at the beginning of a journey of discovery, and AI is the ticket.
Expleo can support you with Artificial Intelligence & Data Science in your automotive projects.
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