How Machine Learning and AI Personalize Automotive Applications

Original Source Here

New information and solutions bring new experiences to users, and these solutions or information are beneficial to both the user and the provider. The taste of the coming exciting news is the main factor keeping users on the path to following new services. For example, suppose you are looking to travel and determine your destination. In that case, you may be interested in all possible ways to answer your question, and depending on the searches done in the past, different types of travel, such as train or flight, can be realized. This piece of information has a significant impact on your decisions.

Amazon and Netflix are working hard to personalize marketing to provide the best experience to their customers. Machine learning is a way to automate the personalization process using all available data from one customer and all customers to serve other customers.

Personalization aims to tailor the process to each individual, and a machine learning model can accelerate and optimize this process by improving its model for each characteristic.

Speech recognition

Artificial intelligence algorithms have many new applications in the automotive industry to assist passengers and drivers in using multimedia or navigation or other areas such as perception and behavior planning. Passengers or drivers like to take advantage of personalization and enjoy automating their habits and knowing the experiences of other passengers or drivers in the same situation.

One of the most basic personalization applications in the car is recognizing the driver’s speech as the owner or regular user or passengers who use the car regularly. Providing special features or desires of a specific driver or user makes travel more enjoyable and saves time and money. If all user information is available on each car, this possibility can be extended to all users. This means that everyone has a secure key to enable speech recognition that is accessible everywhere.

Personalization in services

Artificial intelligence can recommend products or services to customers based on customer profiles. All available and real-time updated customer profiles can help provide services or products that are very useful to customers. For example, you have added new goods to your shopping list, and while you are driving in the city, AI can keep you informed of the nearest stores with the best-priced goods on your list. If AI is aware of your needs and interests, there are many personalization applications for AI. This kind of support is usually what a good friend can do for us, or at least you need time to search and find information about your needs.

Data privacy is an issue that can hinder the use of artificial intelligence as your assistant in the use of any technology. Knowing more about us by AI can be a problem because we do not know who can access our data. However, as more and more applications change our lives, we will likely accept AI access to some of our private data in some way, provided the benefits to us are significant.

Risk analysis and assessment

Autonomous driving requires analyzing large amounts of data and predicting and deciding a proper behavior even better than a human driver. For this reason, the safety of autonomous vehicles is still a critical factor in this technology and will determine whether the technology is mature enough to be launched or not.

Safety experts are aware of this sensitivity and are looking for new solutions. Hazard analysis cannot be performed, as we did before, at design time, as a large amount of uncertainty is in front of an autonomous vehicle. Risks must be analyzed and mitigated at runtime. Personalization could simplify this solution for Level 3 autonomy, where the driver and vehicle share the responsibility for driving. Artificial intelligence algorithms collect information about drivers’ behavior and reactions while driving, and classify drivers and understand which driving tasks should be most controlled. Algorithms can check whether the driver needs to pay more attention to his driving in certain situations.

E2E predictive maintenance

Car troubleshooting allows you to track the car in real-time, identify breakdowns and notify the driver to take the necessary action. For this purpose, a large amount of vehicle data must be stored and analyzed by the AI ​​algorithm. The vehicle informs the driver about possible misbehavior of hardware or software and the driver’s actions to resolve the situation.

To provide end-to-end (E2E) predictive maintenance, AI prediction algorithms require large amounts of data from the vehicle, the owner, and how the owner uses the car. This application is where AI comes in to personalize customers and analyze each vehicle individually. Exchanging real-time experience between customers for a common problem can be another application based on the analysis of individual vehicles. Some applications are not supported and even driven by vehicle manufacturers, but they can be very reasonable and attractive from the customer’s point of view.

Summing up,

Artificial intelligence plays an important role in the automotive industry, from control responsibilities to perform driving tasks to achieve Level 4 and 5 autonomy to applications such as monitoring the driver’s behavior and eyes to ensure that the driver is ready to take over the driving task from AI in level 3 autonomy. Personalization of AI will be an essential part of this transformation, and it is our job to decide for which responsibilities we will use the benefits of AI and which we will not. Unfortunately, this question is not easy to answer because many of the technological, ethical, safety, and data security aspects of data need to be explored to find the best solutions.


Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot

%d bloggers like this: