Automotive AI Integration: On-Board Machine Learning for ADAS Sensor Fusion, Predictive Systems, and In-Vehicle Intelligence - Brossura
Libro 6 di 9: Software-Defined Vehicle Engineering Series
Sinossi
Master the Art of Production-Ready Vehicle Intelligence
As automotive engineering transitions to software-defined architectures, integrating reliable, high-performance machine learning is the ultimate competitive advantage. Automotive AI Integration is the definitive engineering guide to deploying robust on-board ML models inside production vehicles.
Written specifically for automotive embedded engineers, ADAS developers, and AI researchers, this comprehensive manual bridges the gap between theoretical deep learning and real-world physical deployment. Learn how to navigate the complex trade-offs of performance, safety, and hardware constraints using industry-standard frameworks.
Inside this masterclass, you will discover:
- Silicon-Level Optimization: Master neural network inference on leading SoCs like NVIDIA DRIVE Orin, Qualcomm Snapdragon Ride, and Mobileye EyeQ Ultra.
- Advanced Model Compression: Implement model quantization (INT8/FP8), pruning, and TensorRT compilation to fit large models into tight automotive resource budgets.
- Safety & Standards: Design MISRA C++ compliant machine learning pipelines and build robust ISO 26262 safety cases up to ASIL-D.
- Sensor Fusion Architectures: Combine camera Bird’s-Eye-View (BEV), radar point clouds, and LiDAR data inside modern deep neural networks.
- AUTOSAR & OTA Integration: Deploy models as AUTOSAR Adaptive software components and architect secure, failsafe over-the-air (OTA) update pipelines.
- Next-Gen Cockpit Intelligence: Build highly responsive Driver Monitoring Systems (DMS) and deploy local, low-latency LLM voice assistants on-edge.
Whether you are developing Level 2+ ADAS features or architecting Level 4 autonomous systems, this book provides the production-validated patterns, testing paradigms, and validation frameworks you need to write safe, efficient, and regulatory-compliant automotive software.
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Automotive AI Integration: On-Board Machine Learning for ADAS Sensor Fusion, Predictive Systems, and In-Vehicle Intelligence (Software-Defined Vehicle Engineering Series)
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Automotive AI Integration (Paperback)
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Paperback. Condizione: new. Paperback. Master the Art of Production-Ready Vehicle IntelligenceAs automotive engineering transitions to software-defined architectures, integrating reliable, high-performance machine learning is the ultimate competitive advantage. Automotive AI Integration is the definitive engineering guide to deploying robust on-board ML models inside production vehicles.Written specifically for automotive embedded engineers, ADAS developers, and AI researchers, this comprehensive manual bridges the gap between theoretical deep learning and real-world physical deployment. Learn how to navigate the complex trade-offs of performance, safety, and hardware constraints using industry-standard frameworks.Inside this masterclass, you will discover: Silicon-Level Optimization: Master neural network inference on leading SoCs like NVIDIA DRIVE Orin, Qualcomm Snapdragon Ride, and Mobileye EyeQ Ultra.Advanced Model Compression: Implement model quantization (INT8/FP8), pruning, and TensorRT compilation to fit large models into tight automotive resource budgets.Safety & Standards: Design MISRA C++ compliant machine learning pipelines and build robust ISO 26262 safety cases up to ASIL-D.Sensor Fusion Architectures: Combine camera Bird's-Eye-View (BEV), radar point clouds, and LiDAR data inside modern deep neural networks.AUTOSAR & OTA Integration: Deploy models as AUTOSAR Adaptive software components and architect secure, failsafe over-the-air (OTA) update pipelines.Next-Gen Cockpit Intelligence: Build highly responsive Driver Monitoring Systems (DMS) and deploy local, low-latency LLM voice assistants on-edge.Whether you are developing Level 2+ ADAS features or architecting Level 4 autonomous systems, this book provides the production-validated patterns, testing paradigms, and validation frameworks you need to write safe, efficient, and regulatory-compliant automotive software. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798180774019
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Automotive AI Integration : On-Board Machine Learning for ADAS Sensor Fusion, Predictive Systems, and In-Vehicle Intelligence
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Taschenbuch. Condizione: Neu. Neuware - Master the Art of Production-Ready Vehicle IntelligenceAs automotive engineering transitions to software-defined architectures, integrating reliable, high-performance machine learning is the ultimate competitive advantage. Automotive AI Integration is the definitive engineering guide to deploying robust on-board ML models inside production vehicles.Written specifically for automotive embedded engineers, ADAS developers, and AI researchers, this comprehensive manual bridges the gap between theoretical deep learning and real-world physical deployment. Learn how to navigate the complex trade-offs of performance, safety, and hardware constraints using industry-standard frameworks.Inside this masterclass, you will discover: - Silicon-Level Optimization: Master neural network inference on leading SoCs like NVIDIA DRIVE Orin, Qualcomm Snapdragon Ride, and Mobileye EyeQ Ultra.- Advanced Model Compression: Implement model quantization (INT8/FP8), pruning, and TensorRT compilation to fit large models into tight automotive resource budgets.- Safety & Standards: Design MISRA C++ compliant machine learning pipelines and build robust ISO 26262 safety cases up to ASIL-D.- Sensor Fusion Architectures: Combine camera Bird's-Eye-View (BEV), radar point clouds, and LiDAR data inside modern deep neural networks.- AUTOSAR & OTA Integration: Deploy models as AUTOSAR Adaptive software components and architect secure, failsafe over-the-air (OTA) update pipelines.- Next-Gen Cockpit Intelligence: Build highly responsive Driver Monitoring Systems (DMS) and deploy local, low-latency LLM voice assistants on-edge.Whether you are developing Level 2+ ADAS features or architecting Level 4 autonomous systems, this book provides the production-validated patterns, testing paradigms, and validation frameworks you need to write safe, efficient, and regulatory-compliant automotive software. Codice articolo 9798180774019
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