More than just autonomous driving: AI in the automobile industry
February 12, 2019
The vehicle of the future as a fully autonomous “computer on wheels” with no pedals or steering wheel is still a long way off. Artificial intelligence plays a decisive role in autonomous driving. For the car industry, artificial intelligence already lends itself to many other areas – for instance in logistics and production, as well as customer loyalty.
AI for autonomous driving
Automobile traffic is an exceptionally complex system. Car drivers effectively register thousands of impressions within short periods of time. These impressions are processed in the brain. The task of artificial intelligence is to mechanically process and correctly integrate these sensory data. The quality of artificial intelligence is essentially dependent on the amount and the reliability of the training data. For example: The more people are involved in spotting silhouettes of people on photographs, the sooner an algorithm will be put in a position to provide exact predictions in real traffic situations.
The crowd provides reliable AI training data for systems in which artificial intelligence is employed. clickworker offers customized solutions for these areas.
Artificial intelligence is based on regular input – it is the learning matter for the machines. Artificial intelligence can for instance be trained by simulating hazardous traffic situations. Human reactions and their consequences are therefore the data basis for machine learning. The software analyzes all relevant information and selects the best reactions. Artificial intelligence has a decisive advantage: unrestricted by emotions, it selects the most effective action in each case.
Big Data for board systems
Artificial intelligence recognizes the profiles of the driver and the occupants. The more accurate the identification, the better the vehicle will adapt itself to the users. Machine learning learning specific behavior patterns makes driving easier, more effective and safer. Simplified utilization of vehicles obviously involves optimal sensor technology, which gathers various types of information:
Technical data regarding the condition of the vehicle,
Statements made by the driver and the occupants,
Environmental data (temperature, air humidity, light conditions, etc.)
Information supplied by the carmaker (for instance regarding the availability of specific spare parts).
The board manual of the vehicle, in the form of an XML file, provides the data for the technical knowledge.
NLP is the interface for interactive communication with the driver, for instance when the latter asks: “Why is this warning lamp on now?”
The data provided by this communication in an actual situation (including course of events, inquiries, time needed and success or failure of the communication) are the basis for continual improvements to the system. The machine itself learns based on the application.
Natural language as the basis for AI? The international crowd of Clickworkers can provide large amounts of audio datasets in numerous languages at a reasonable price.
Production robots learn faster
Robots and machines, which are utilized in the production of automobiles automatically learn on the basis of trial and error what work methods are particularly effective. For instance, the automated bin-picking of the parts to be handled is becoming increasingly ingenious and therefore less time-consuming. The software optimizes an increasing number of procedures in the automobile production industry. The neuronal network used during the production process is based on flexible connections. These systems organize themselves independently of firmly nested components. The machines learn from errors by continually optimizing the network of connections.
Deep Learning in logistics
Carmakers already fall back on artificial intelligence during the manufacturing process. Which, for example, enables an extensive production network that car industry suppliers are also involved in.
Artificial intelligence independently interprets problems and develops customized solutions. Algorithms that are based on artificial intelligence do not work according to rigid guidelines, instead they verify results based on a continuously growing database, therefore improving themselves. This deep learning principle enables machines to reach increasingly exact predictions. For instance in logistics: Based on vast amounts of available data, which influence the delivery of parts needed for production of vehicles, computers independently calculate the best transport routes. In periods of just-in-time deliveries these skills are worth their weight in gold.
The principle of deep learning shows that artificial intelligence must be trained. Today, the crowd – globally connected people who earn their money performing micro-tasks on platforms – is delivering the data basis for systems that constantly continue to advance and successively develop increasingly detailed models.
Intelligent systems for customer loyalty
Artificial intelligence is also a means of achieving customer loyalty. Internal vehicle systems indicate early on what areas need servicing. At the same time, the system provides information regarding where the respective service required or the necessary spare part is immediately available. The cloud is obviously an ideal database, which can function as a co-pilot with a phenomenal knowledge base in every networked vehicle. At the same time, the individual driving behavior of the respective driver teaches the software how to make predictions and how to indicate optimization potential.
The car of the future
Electromobility, artificial intelligence, digitalization, Big Data, and Deep Learning – the mobility of the future will be quite unlike what it is today. The cloud and global networking will further strengthen the amount of available data and possibilities to analyze them. In the age of hyper-networked driving, which lies perhaps not quite so far in the future, our relationship to cars as a means of transport will undergo a radical change. And artificial intelligence is being utilized in an increasing number of fields – from planning to autonomous driving.
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