Algorithm & SoC design for automotive vision systems : for smart safe driving system
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Algorithm & SoC design for automotive vision systems : for smart safe driving system
Springer, c2014
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内容説明・目次
内容説明
An emerging trend in the automobile industry is its convergence with information technology (IT). Indeed, it has been estimated that almost 90% of new automobile technologies involve IT in some form. Smart driving technologies that improve safety as well as green fuel technologies are quite representative of the convergence between IT and automobiles. The smart driving technologies include three key elements: sensing of driving environments, detection of objects and potential hazards and the generation of driving control signals including warning signals.
Although radar-based systems are primarily used for sensing the driving environments, the camera has gained importance in advanced driver assistance systems (ADAS).
This book covers system-on-a-chip (SoC) designs-including both algorithms and hardware-related with image sensing and object detection by using the camera for smart driving systems. It introduces a variety of algorithms such as lens correction, super resolution, image enhancement and object detections from the images captured by low-cost vehicle camera. This is followed by implementation issues such as SoC architecture, hardware accelerator, software development environment and reliability techniques for automobile vision systems.
This book is aimed for the new and practicing engineers in automotive and chip-design industries to provide some overall guidelines for the development of automotive vision systems.
It will also help graduate students understand and get started for the research work in this field.
目次
- Preface. Chapter 1 Introduction
- Jae-seok Kim, Hyun-chul Shin. 1.1 Introduction to the advanced driver assistance system. 1.2 Industrial Developments for ADAS. 1.3 System-on-chip platform architecture for automobile vision systems. References. Chapter 2 Lens Correction and Gamma Correction
- Sang-Bock Cho. 2.1 Lens Correction. 2.2 Gamma Correction. References. Chapter 3 Super Resolution
- Hyo-Moon Cho. 3.1 Introduction. 3.2 Observation model. 3.3 Survey of the Super Resolution algorithms. 3.4 Novel Super Resolution registration algorithm based on Frequency. 3.5 Conclusion. References. Chapter 4 Image enhancement for improving object recognition
- Jae-Seok Kim. 4.1 General Image Enhancement Techniques. 4.2 Image Enhancement Techniques for Automobile Application. References. Chapter 5 Detection of Vehicles and Pedestrians
- Hyunchul Shin, Irfan Riaz. 5.1 Introduction to Vehicle/Pedestrian Detection. 5.2 Vehicle Detection. 5.3 Pedestrian Detection. 5.4 Night-time Pedestrian Detection. References. Chapter 6 Monitoring Driver's State and Predicting Unsafe Driving Behavior
- Hang-Bong Kang. 6.1 Introduction. 6.2 Driver Drowsiness Measurement. 6.3. Driver Distraction Detection. 6.4 Predicting Unsafe Driving Behavior. 6.5 Discussion. 6.6 Conclusions. References Chapter 7 SoC Architecture for Automobile Vision System
- Kyounghoon Kim, Kiyoung Choi. 7.1 Automotive Applications. 7.2 Architectural Consideration for Vision. 7.3 Example - Pedestrian Detection. 7.4 Comparison of COTS Architectures. 7.5 More on GPU. 7.6 Comparison of VLIW and COTS Architecture. 7.7 Memory/Bus Requirement. 7.8 Vision Processors. 7.9 Yet another Approach. 7.10 Conclusions. References. Chapter 8 Hardware accelerator for feature point detection and Matching
- Jun-Seok Park, Lee-Sup Kim. 8.1. Introduction to interest point detection and matching. 8.2. Interest point detection hardware with joint algorithm-architecture optimization. 8.3. Unified Datapath. 8.4. Chip implementation. 8.5. Application. 8.6. Conclusions. References. Chapter 9 Software Development Environment for Automotive SoC
- Jeonghun Cho. 9.1. Introduction. 9.2. AUTOSAR architecture. 9.3. Demonstration of AUTOSAR ECUs. 9.4. Conclusions. References. Chapter 10 Reliability issues for automobile SoCs
- Sungju Park. 10.1 Introduction. 10.2 Conclusions. References.
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