Automotive safety systems rely on the ability to make sense of the information gathered by cars’ surroundings.
Cameras, radar, LiDAR, and other sensors generate data, and AI and machine learning can be leveraged to extract valuable information from the data to inform safety decisions.
A simple architecture would look similar to this:
Sensors ➡️ Data ➡️ AI Inference ➡️ Risk / Safety Detection ➡️ Safety Response
The important engineering consideration is not simply gathering data from sensors, but also processing this information reliably, within the needed time constraints, and generating actionable information that informs a safety risk or system response.
From Sensor Data to AI Inference
A car generates information on the environment around it: other cars, pedestrians, road signs, lane markings, and other objects.
This information can be processed using AI models to detect objects and patterns; for example, computer vision can be leveraged to analyze the information from cameras, while other sensor data streams can also be incorporated to build a richer understanding of the situation at hand.
A production-level system will also need to account for issues such as sensor data quality, synchronization between various data streams, processing latency, and more.
Driver Monitoring
Another area in which AI can be leveraged is driver monitoring.
In this case, visual information from cameras can be processed to analyze the driver’s behavior. Particularly, head pose estimation and eye direction can be used to determine whether a warning needs to be issued to the driver.
What this example shows is that while a standard computer vision model would be able to identify objects and scenes in the image, an AI-powered driver monitoring system needs to be able to reason about this stream of information in real-time to make decisions about the driver’s safety.
Collision Detection and Prevention
AI can also be applied in the context of collision detection and prevention: object detection models can be used to determine potential obstacles and analyze the risk of collision, informing features such as automatic brake activation and safety alerts.
The general architecture is similar to the examples above: data is gathered and processed, and the results are used to assess potential safety risks and manage the car’s behavior accordingly.
It is important to note that the success of such a system depends not only on the performance of the AI model but also on the quality of sensors, the processing power available, system latency, and the ability to integrate such a system into the car’s control architecture.
AI Beyond the Vehicle
The role of AI is not limited to the cars themselves.
Many automotive manufacturers are beginning to leverage AI solutions for their manufacturing processes: AI can be used to gather information on the performance of various machines, analyze patterns and detect anomalies, and improve the manufacturing workflow.
This is where AIoT comes into play: connected devices generate data, which can then be processed by AI systems to identify patterns and generate actionable insights.
A general manufacturing architecture would involve connecting devices and systems with the cloud, where data is processed and filtered, and then generating meaningful information that can be used to improve the manufacturing process.
If you would like to see an example of how AIoT, industrial connectivity, and automotive manufacturing can be combined, this blog from OEMNex AI covers this topic in more detail from an automotive manufacturing perspective.
What Developers Need to Consider
When building automotive AI applications, it is important to remember that there is more to the process than simply building a machine learning model.
Depending on the use case, developers and engineers may want to consider such factors as the quality of data, processing latency, the use of edge versus cloud processing, the integration of multiple sensors, and the ability to operate such a system within a larger framework.
These considerations are what make automotive AI development a multidisciplinary endeavor that goes beyond simply training a machine learning model.
In broader terms, building connected and intelligent automotive systems involves addressing the challenges of computer vision, embedded systems, networking and edge computing, and industrial IoT.
The primary technical challenge stems from the need to generate actionable insights in a timely manner and apply them to real-world systems.
As automotive systems grow more connected and capable, the combination of AI, computer vision, and connectivity will generate a variety of new technical challenges and opportunities for developers and manufacturers who seek to innovate in the space.
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