Accelerating Development: Machine Learning in Automotive Software

Machine learning (ML) is being deployed by a wide range of sectors, improving the optimization of software as well as delivering more accurate software. The automotive industry is one such sector that is exploring the benefits of ML and deep learning, creating effective solutions such as driver assistance systems.

In this article, we will discuss automotive machine learning, how ML in automotive software is accelerating the development of groundbreaking solutions, the challenges faced, and what we can expect in the future.

Machine learning is a subcategory of AI that allows computers to learn new information without specific human input. ML uses statistical methods to understand patterns in data, allowing it to perform complex tasks and solve problems often more efficiently than traditional methods.

For example, ML is the technology that powers chatbots, language translation tools, predictive text as well as targeted ads on social media platforms. It is also used to provide suggestions based on users’ watching and listening history for platforms such as Netflix, YouTube, and Spotify. In fact, so widespread are its applications that it even powers autonomous vehicles.

Back in the 1950s, one of the early developers of AI, Arthur Samuel, described the technology as “the field of study that gives computers the ability to learn without explicitly being programmed.” A concise summary that can still be applied to machine learning to this day.

Although machine learning has broad capabilities, there are complexities and constraints in some tasks due to the nature and complexity of the data, among other factors. However, with the current rate of development, its capabilities are likely to become much more impressive in the coming years. In automotive machine learning especially, the advancements in AI have resulted in incredibly accurate software to help prevent accidents and warn drivers of danger.

Unfortunately, one downside to machine learning in automotive and other sectors that must deliver high levels of safety is the amount of time it takes to develop, train and tune these ML models. Some level of human intervention is still required in certain scenarios. But, ML software aims to let the algorithm learn for itself over time, gradually reducing the need for human input.

The different types of ML are:

• Supervised models: Supervised models are trained using labeled datasets, where both the inputs and desired outputs are provided. . These models improve their performance as they adjust their internal parameters to minimize the difference between the predicted and actual outputs. For example, artificial intelligence in automotive software could be trained to read road signs that have been labeled by humans.
• Unsupervised models: Unsupervised models identify patterns and trends in unlabeled data that developers are not specifically looking for. For example, a machine-learning car sales tool could examine sales data to identify customer trends.
• Semi-supervised learning: This approach combines both labeled and unlabeled data for training, typically using a small amount of labeled data with a large amount of unlabeled data. Semi-supervised learning is beneficial when the cost associated with labeling is too high."
• Reinforcement learning: These models learn by interacting with an environment, where they take actions that are either rewarded (encouraging the model to repeat them) or punished (discouraging repetition). A good example of this is autonomous vehicle software which informs the model that it has made the correct decision during training. Over time, the model will learn what is the best option to take in certain situations.

Machine learning in automotive programs has seen significant growth, becoming one of the most propitious sectors in terms of utilizing AI to deliver advanced solutions. ML can be used to improve safety, reduce emissions, shorten drive times, and more.

Global companies, such as BMW, Mercedes-Benz, and Tesla, have already developed powerful machine learning algorithms to improve the performance of autonomous vehicles, detect potential accidents, and provide useful status and location updates for drivers.

Improving safety is, of course, the main focus for automotive software, with machine learning in cars helping to reduce accident rates and warn drivers of potential dangers. Advanced driver-assistance systems accurately identify potential hazards and issue alerts by analyzing data that is collected from sensors and cameras.

Reducing emissions is another key use case for machine learning in automotive deployments, with ML algorithms identifying optimal routes and reducing fuel consumption. This is achieved by identifying time and fuel-saving solutions by analyzing a range of data and monitoring driving patterns. As well as reducing emissions, this can result in significant financial savings for consumers.

Other use cases of machine learning in the automotive sector include autonomous driving systems and predictive maintenance.

Although automotive machine learning is still in its early stages, the possibilities are endless as the technology evolves and organizations invest more in innovation.

With any technology comes advantages and limitations relating to effectiveness, accuracy, cost, and other factors that could impact the final product. In this section, we will look at the benefits and drawbacks of machine learning and artificial intelligence in automotive software.

Here are a few use cases of how automotive machine learning can be used to deliver advanced solutions.

• A popular example of machine learning in cars is its use in tools that develop advanced driver-assistance systems (ADAS) that warn drivers about potential hazards on the road.
• Another use case is ML software that optimizes driving schedules and vehicle routing, helping to reduce carbon emissions and fuel consumption.
• A third example is machine learning used to detect and diagnose issues with a vehicle before they impact safety or require expensive repairs. This also reduces the amount of time a vehicle might be out of action, which is particularly useful in the field of motorsport.
• Automotive AI can study a driver’s preferences to adjust a car’s settings and provide a personalized driving experience.
• Over time, machine learning can help make the automotive industry cleaner and vehicles more efficient. AI can be used in hybrid cars to switch between fuel types based on their cost-effectiveness, thus accelerating the switch to greener cars that are powered by electricity.
• ML algorithms can also make real-time adjustments to the settings of cars, reducing or increasing engine power when needed.
• By making driving safer, the cost of insurance may also be reduced in the future due to fewer accidents. Insurance is not always cheaper for electric cars, but thanks to systems like ADAS which are common in EVs, they may become more affordable in the future.

Let’s look at some of the downsides of automotive ML.

• Machine learning requires significant amounts of data to learn, make predictions, and perform tasks accurately. The key challenge in terms of data is the sharing of information, with data often siloed between individual companies and organizations. This means machine learning models do not have access to all the data they might need to work to their full capacity.
• The constant changes in the industry also pose a challenge, with new automotive regulations introduced that might differ from country to country. Changing technologies can also make certain models obsolete. As such, the upkeep of these models needs a lot of investment.
• Yet another challenge with automotive machine learning is making key decision-makers understand the benefits of the technology. Many leading companies have welcomed ML, but business leaders still need sound understanding to allow them to make informed decisions.

Unfortunately, providing an explanation of an AI algorithm or ML model in simple terms is not straightforward. Many ideas may not make it to the development stage as it is not clear if they will provide a competitive edge.

Taking a broader view and focusing on implementing AI within the automotive industry as a whole, there are five common issues that need to be overcome if development of ML deployments is to be accelerated.

Rushing through the planning stages of an AI algorithm project is often why many initiatives falter in the early stages.. It is vital to ensure the fundamentals are in place before sourcing a data partner to scale a project. Overlooking factors, such as how the data partner will receive datasets and how the annotated data will then be viewed, is an all too common problem.

Data must also meet quality standards to be used in commercial products which makes choosing the right data partner a laborious process. The right programs are also required to view specific annotated datasets (e.g., LiDAR data). Without them, it will not be possible to assess the data to ensure it has been annotated correctly.

Localization is a key consideration for automotive companies when it comes to developing new software that uses AI. Products need to appeal to global markets. This means the products need to work in different languages, take into account different cultures, and be accessible to different demographics. This creates a personalized experience that provides added value to the consumer.

Automotive AI projects can be extremely complex and their development may be underestimated. If the feature space and categories within the data are especially large, the project may pose significant complexity and might require too many resources to be viable.

A common mistake is failing to provide monthly updates to AI deployments so that they can keep up with changing conditions, which is particularly important in the automotive industry. This could involve a transformation in use cases or model drift, which refers to a model becoming less accurate over time. Failing to consider the need for continued training and updating of models throughout the project can severely impair the project's success and limit the ROI.

Much of the data collected in the automotive sector is sensitive and needs to be protected. A data breach can have a significant financial impact that can damage the long-term reputation of a business. Securing AI software, therefore, needs to be at the forefront of its design. Security measures need to be put in place to assess all vulnerabilities, from secure user access to protecting the various endpoints and cloud deployments.

Almost all ADAS features can be improved with machine learning including:

• Object detection, classification, and response
• Parking assistance and parallel parking assistance
• Lane detection and correction
• Lane change assistance
• Backup assistance.

To create an effective ADAS, machine learning models must be multifaceted to focus on specific functions. Each facet must interact with one another to provide an overall solution while still doing its job in an optimized way.

For example, object recognition uses the vehicle’s camera to spot objects on the road. One system will identify the object, another will calculate its distance, and a third will correct the steering to avoid a collision. The steering correction stage of this process will interact with other systems, such as the side and rearview mirrors, to determine whether the brakes need to be applied or if the vehicle should change lanes.

However, if ADAS systems are to become universal and continue to improve, more than just their functionality needs to be considered.

Machine learning automotive software can significantly enhance the customer experience through personalized offerings based on individual drivers.

The driver’s user profile contains preferences that are analyzed by ML algorithms to create a baseline of the driver’s ideal settings. This can include preferred temperatures, with adjustments made depending on the external temperature to ensure the driver is comfortable at all times.

For electric vehicles, ML can also evaluate driving behaviors to predict fuel consumption. In addition, the software can make recommendations to better preserve battery life and maximize fuel efficiency.

One of the most advanced use cases of machine learning in cars is the ability to predict a car’s maintenance and service needs (intelligent maintenance). Traditionally, maintenance alerts are based on factors, such as how many kilometers have been driven or the amount of time between each service, issuing a prompt when a milestone has been reached. This means a large number of factors are not being taken into account which makes the system somewhat unreliable.

In an electric car, unsupervised machine learning can identify elements that drain the battery quickly based on real-time data from the electrical control unit (ECU). The system can help increase the lifespan of the vehicle’s battery and identify parts that may need replacing.

Artificial intelligence and machine learning are playing a key role in automotive software, improving safety, making vehicles more eco-friendly, and offering personalized experience to drivers. Advanced driver-assistance systems and intelligent maintenance have seen significant improvement in the last few years, particularly in electric vehicles. This has helped improve battery life, identify issues, and create better driving experiences.