How to Create, Optimize, and Scale Data Pipelines for Automotive AI
When you think about artificial intelligence (AI) in the automotive industry, future roadways full of self-driving cars may immediately come to mind. While this isn’t our reality quite yet, there are numerous AI advancements in the auto industry that have enhanced safety as well as the in-car experience. As auto industry players see this success, they, too, are turning to AI in the race for a mass-market fully autonomous vehicle.
Companies committed to pursuing AI should set their sights on creating automated data pipelines for automotive AI as part of their strategy. While there are many complexities involved in a machine learning pipeline, setting one up enables faster deployments and more efficient, well-tested models. For companies, this means gaining a competitive edge and the ability to scale quickly in a rapidly-evolving market.
Trends in Automotive AI Use Cases
Self-driving cars are just one type of focus for AI in the automotive industry, with many other developments being made in safety and efficiency. These are the latest trends to be aware of:
Automation levels in cars are split into five categories, starting from Level 0, where the driver fully controls the vehicle, to Level 5, where the vehicle is entirely autonomous, and no driver control is needed. Cars at Level 0 are the most common on the roadways now, but forecasts expect change. By 2024, cars with some automation level will represent more than half of all vehicles produced, with Levels 1 through 3 seeing the most significant growth. We’re still some time away from seeing fully autonomous, Level 5 car in commercial production, however.
In-Cabin Monitoring and Assistance
The most dangerous thing about driving continues to be the driver (over 90% of crashes are caused by driver error). Drivers not paying attention is a significant cause of accidents, which is why in-cabin monitoring is touted as a significant step forward for safety. Driver monitoring systems (DMS) can detect what’s happening inside the cabin and make adjustments to the environment as needed. For example, they detect whether a driver is looking at the road, how many passengers are in the car, their sizes, and weights; can adjust airbag sizes accordingly; apply brakes if needed, and signal if a passenger isn’t wearing a seatbelt.
In addition to safety, in-cabin assistance offers a more enhanced experience for the driver and passengers. It can adjust the cabin to fit the individual’s preferences (for example, by automatically adjusting seat position and climate control). As cars advance up automation levels, expect to see these systems improve as well.
Driving isn’t just about safety, but also about the overall experience. The in-car entertainment market is projected to be worth over $20 billion by 2026. An infotainment system includes advanced features that provide not only entertainment but also information and communication services. Voice commands and gesture-based interactions are quickly replacing manual keypads to interact with the vehicle.
In-Cloud and Collective Intelligence
A car’s on-board capabilities need not limit autonomous driving. With cloud technologies, we can aggregate information from multiple vehicles on essential factors like road conditions and traffic. Cars can effectively “communicate” with each other on upcoming decisions to reduce the chances of collisions.
AI-Based Automation in Manufacturing
AI in auto manufacturing helps automate manufacturing processes and improve quality control by detecting defects faster and more precisely than human counterparts. AI may also augment car safety experiments in simulated environments.
What Do Data Pipelines for Automotive AI Look Like?
Building out a comprehensive data pipeline is essential for long-term success in the automotive space. Generally, data pipelines for automotive AI should include the following five steps:
- Data Collection
Synchronized sensor data is collected in the car in-situ and ported to a central processing unit (CPU). Sensor data may be collected from cameras, which detect texture and color of traffic markings and signs; LIDAR, which detects shapes of pedestrians, cars, and other structures; and RADAR, which detects object positions and speeds. Auto manufacturers may choose to build their platforms or buy a specialized data collection platform from third-party vendors.
- Data Annotation
Ideally leveraging a crowd of diverse individuals, sensor data is annotated to match model inputs and outputs. Relevant features like signs, pedestrians, roadways, and other objects in the image data are labeled. The annotated data should be split into training sets, validation sets, and testing sets, as these collectively serve to improve model accuracy.
- Model Training
The algorithm is fed the annotated training data, and the output is validated against the inputs. For each sensor type, specialized model architectures are working together.
- Model Testing
Contender models are tested against the champion model (the model that performs the best) using A/B testing methods. If a contender model outperforms the champion model, the contender becomes the champion. This stage is also where testing occurs inside the vehicle in realistic environments.
- Model Deployment
After testing, the compressed model is ready for installation. A target hardware platform must be selected, for example, Coral System-on-Module. Following deployment, continue to improve the model through retraining and testing.
How to Optimize a Data Pipeline
Once you have a data pipeline in place, the next step is to optimize it for faster deployments and heightened scalability. One way to maximize your data pipelines for automotive AI is by integrating with APIs, intending to increase throughput by automating data movement in the channel. The more automation you implement, the quicker you’ll be able to build production-ready models. Another way to optimize is by applying active learning techniques to your model. You’ll want to prioritize data to annotate based on where the model is getting confused and producing low-confidence or erroneous predictions.
As you create your pipelines, be sure to avoid common pitfalls. Data drift is a phenomenon that results in stale, increasingly ineffective models. Using active learning to retrain and tune your model regularly in production will help mitigate this effect. Equally important, always have scalability in mind; for instance, when scaling into different markets, remember that you may need to train your model on new geography or climate data.
Building fully-optimized, automated data pipelines will increase your models’ scalability and enable you to deploy with confidence and go to market faster. As competition in the automotive AI space intensifies, organizations that carefully plan and execute their AI strategies and processes will come out ahead.