Five Artificial Intelligence Challenges in Automotive Applications
Challenges are part and parcel of life. This is especially true for companies in the automotive industry focused on the production of self-driving cars. Although artificial intelligence is the driving force of this revolution, numerous issues will be faced by the key players trying to implement it. It is only through prior preparation and the formulation of working strategies that these challenges can be mitigated. But first, we need to know what some of these issues are. Below are five challenges they should expect in the use of artificial intelligence for automotive applications:
Handling the Basics
In the excitement of commencing a big project like the implementation of AI in vehicles, it is easy for individuals to forget or overlook the fundamentals. In using artificial intelligence, one of the most basic requirement is data required to train the machine. The training data for machine learning is essential in ensuring the final product is of the required standard. Since the principle applied is “garbage in, garbage out”, it is important that the training data is outsourced from qualified and diverse individuals who can assure the desired quality.
Within the automotive sector, localization is imperative. With different markets to satisfy, the designing and development of artificial intelligence must consider various factors. The product should be customized to meet the expectations and needs of various demographics, making it important for that the designers take into consideration the demographics, norms and customs of specific markets. Getting a team or partner with the skills to create local language guides and voice personas will go a long way in ensuring success. Luckily, the availability of cloud translation management systems present an ideal solution for implementing various languages.
Developing autonomous vehicles is not simple as it requires top-notch skills and technologies from different sectors. Due to this level of complexity, companies in this sector need to have a working strategy on how they will handle such challenges. For instance, an organization could assess its capabilities and outsources external expert help for the processes that cannot be handled in-house. It is also important to liaise with reliable partners to provide direction, specialist view and reliable gadgets such as sensors and cameras.
The sensitive nature of artificial intelligence poses some very serious security concerns. The data collected is sensitive and will require effective data security measures to prevent serious breaches. Putting in place solutions for data protection will ensure your project does not suffer the heavy losses and fallback from a data breach. Furthermore, complete automation of cars will greatly rely on electric devices and the internet, which are susceptible to all manner of attacks. Protecting the whole artificial intelligence system from external harm and intrusion will therefore prove to be a challenge.
Just like in any other sectors, it is the changing conditions and continuous improvement that will allow the organizations to survive in the tough competitive environment. This means that the development process will most likely be a continuous project, involving regular updates and model retraining. As technology in various sectors continues to advance, they have to be incorporated into the system or else the company will lag. The continuous collection and improvement of data while leveraging it against human evaluators will help you keep your artificial intelligence at the forefront of improving technologies.
The best way to achieve the ultimate artificial intelligence for the automotive industry is by beating the discouraging odds. To do that you must consider and use available resources to address the challenges before facing them. Using quality training data will ensure your model learns from the most accurate and relevant human scenarios.