Developing real-time AI systems for automotive edge applications
An automotive technology company developing high-speed sensor systems for autonomous and assisted driving needed AI models capable of detecting objects in real time on resource-constrained edge hardware. We developed compact AI models, FPGA deployment capabilities, and safety-compliant development processes to support the product's path toward market integration.
About the client
An automotive technology company developing high-speed sensor systems for autonomous and assisted driving applications.
The product required AI-based object detection capable of operating directly on FPGA-based edge systems, where computational resources and latency were tightly constrained. The technology also needed to be developed within established automotive safety and software engineering processes.
The engagement focused on developing compact AI models, supporting their deployment on edge hardware, and establishing the development and documentation practices required for automotive-grade productization.
The business challenge
Object detection for autonomous and assisted driving requires AI models that can interpret sensor data quickly enough to support real-time decisions. Deploying these models on edge hardware adds further constraints around model size, computational efficiency, and inference latency.
The client also needed to develop and manage large volumes of labelled training data consistently. At the productization stage, the development process had to align with automotive safety and software engineering standards, adding requirements around documentation, traceability, and process discipline.
Key Challenges
- Creating large, consistently labelled datasets for supervised AI model training.
- Developing compact AI models capable of high-speed inference.
- Deploying object detection models on FPGA-based edge systems under strict latency constraints.
- Maintaining reliability within resource-constrained embedded environments.
- Aligning development processes with ISO 26262 automotive safety requirements.
- Following ASPICE processes and documentation requirements for product development.
How we solved it
We developed and optimized AI models specifically for high-speed inference on automotive edge hardware.
The approach began with the training data requirements, supporting large-scale annotation workflows to create the consistent datasets required for supervised model development. We then designed lightweight AI models optimized for FPGA-based deployment, balancing model capability with the computational and latency constraints of the target edge environment.
To support productization, we also established structured development and documentation workflows aligned with ASPICE and ISO 26262 requirements. This helped connect the technical development process with the safety and process requirements expected in automotive product development.
Toolchain optimization and close engineering collaboration further reduced the time required to move the technology toward market integration.
Solution Highlights
- Supported large-scale training data annotation for supervised learning.
- Designed lightweight AI models for resource-constrained edge environments.
- Optimized models for high-speed inference on FPGA systems.
- Developed deployment toolchains for efficient edge integration.
- Aligned development workflows with ASPICE requirements.
- Supported ISO 26262-aligned automotive safety processes.
- Structured documentation and development processes for productization.
- Reduced development time through toolchain and workflow optimization.
Business outcomes
Business Impact
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The resulting AI system was capable of performing live object detection on FPGA-based edge hardware, providing the technical foundation required for integration into the client's automotive product.
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The development process also achieved alignment with the required automotive standards, supporting the product's path toward regulatory approval and market integration.
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Through optimized toolchains and efficient collaboration, the development timeline was shortened by 3.5 months, accelerating product delivery while maintaining the required safety and quality benchmarks.
How might this challenge look in your industry?
Although this engagement focused on automotive object detection, the underlying challenges of deploying AI at the edge, meeting strict latency requirements, and engineering safety- or compliance-sensitive systems apply across industries where AI must operate reliably close to physical systems.
- Industrial Automation
- Deploying computer vision and AI models directly on machines for real-time quality inspection, anomaly detection, and process control.
- Manufacturing
- Running AI-based inspection and monitoring systems at the edge where response time and operational reliability are critical.
- Energy & Utilities
- Processing sensor data locally to detect equipment anomalies and support rapid responses across distributed infrastructure.
- Healthcare
- Deploying AI on medical and diagnostic devices where latency, reliability, and regulatory requirements constrain system design.
- Robotics
- Running perception and decision-making models directly on robots where real-time inference is required for safe operation.
- Telecommunications
- Processing network and infrastructure data closer to the edge to support real-time monitoring, anomaly detection, and automated response.
- Consumer Electronics
- Integrating AI into resource-constrained devices while balancing model performance, compute requirements, power consumption, and user experience.
- Aerospace & Defense
- Deploying AI on embedded systems where real-time processing, reliability, traceability, and stringent engineering standards are critical.
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Facing a similar challenge?
Whether you need to deploy AI on constrained hardware, build real-time computer vision systems, or engineer AI products where safety, latency, and reliability are critical, we can help develop and productize the technology from model development through edge deployment.
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