Modern cars with ADAS features contain powerful computing resources. On-device AI is at the heart of these systems — running inference locally for driver monitoring, sensor fusion and vehicle control. But what happens to all this on-device AI compute when an emotionally intelligent car is parked and no longer being driven? Let us explore.
Cars with advanced ADAS features acquire data from multiple sources — arrays of cameras, LiDAR, RADAR and on-board ECUs — and must perform immense amounts of processing. This demands powerful edge computing AI hardware. If you take the leader in this segment, Tesla vehicles are equipped with Autopilot and Full Self-Driving (FSD). On a conservative estimate, the Tesla FSD computer has 72 teraflops of processing power — significantly more powerful than many personal computers and even some supercomputers.
Tomorrow, with emotionally intelligent cars, this on-device AI capacity will continue to grow by leaps and bounds. While commercial vehicles are in use for a significant portion of the day, passenger vehicles are typically used for a very limited time. Studies indicate that cars are parked 95% of the time. So, how can we leverage this idle on-device AI compute effectively? We can explore potential options, some of which are currently in research.
Parked emotionally intelligent cars can share their idle on-device AI compute, storage and communication resources to create a distributed edge computing AI network. Open standards could enable third-party applications to run on on-board computers — turning the parked car into an edge node in the city's AI infrastructure.
Even when parked, emotionally intelligent cars can collect and share real-time traffic data in the vicinity, helping to optimise traffic flow and reduce congestion. On-device AI processes this data locally before sharing only the relevant insights — reducing bandwidth consumption while contributing to the broader edge computing AI ecosystem.
Emotionally intelligent cars can monitor air quality, noise pollution and other environmental factors, providing valuable data for urban planning. On-device AI makes this possible without transmitting raw sensor data to the cloud — a privacy-preserving, bandwidth-efficient approach.
Parked emotionally intelligent cars can revisit and process the data accumulated over past driving sessions, retrain their models and improve autonomous vehicle algorithms. This edge computing AI approach to continual learning is far more efficient than sending raw data to centralised cloud servers.
For OEM internal use, parked cars can simulate driving scenarios and test autonomous driving algorithms when idle — using the on-device AI hardware that would otherwise sit unused.
Protecting sensitive data and preventing unauthorised access to vehicle systems is crucial. On-device AI must be secured against both physical and network-based attacks to ensure that the parked car does not become a vulnerability in the edge computing AI network.
Developing clear regulations and standards for the use of vehicle resources is necessary to ensure safety and compliance — both for the on-device AI systems themselves and for the data they generate and share.
Perhaps the most important challenge is balancing the use of on-device AI and edge computing AI resources with energy efficiency — avoiding draining the car's battery while parked. Intelligent power management must be built into both the hardware and the AI operating system of emotionally intelligent cars.
Emotionally intelligent cars will eventually address these challenges and unlock the potential of on-device AI in parked vehicles. Together, connected cars equipped with on-device AI form a distributed intelligence grid — contributing to a more sustainable world and unlocking new opportunities for innovation. Embien's edge computing services help OEMs design the on-device AI architectures and edge computing AI software stacks that make this vision achievable.
The on-device AI that enables the personalized driving experience while driving does not need to be idle when the car is parked. Preference learning, model updates and scenario simulation can all run in the background — so the next time the driver takes the wheel, the personalized driving experience is even more precisely tuned to his current state. Automotive cybersecurity services ensure that this background processing is protected, maintaining the integrity of on-device AI and edge computing AI systems across the vehicle's lifetime.
Explore how Embien's product engineering services design the on-device AI and edge computing AI architectures that power emotionally intelligent cars — both during driving and when parked.
Embien's AI and ML development services build the on-device AI models and edge computing AI pipelines that enable emotionally intelligent cars to learn, adapt and process data locally.
A case study on Embien's expertise in connected edge computing AI devices — the same on-device AI architecture principles applied to emotionally intelligent cars and distributed vehicle networks.