國立虎尾科技大學 |

Machine learning and optimization techniques for automotive cyber-physical systems

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine learning and optimization techniques for automotive cyber-physical systems/ edited by Vipin Kumar Kukkala, Sudeep Pasricha.
其他作者:	Pasricha, Sudeep.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xv, 789 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Cyber-Physical Systems. -
電子資源:	https://doi.org/10.1007/978-3-031-28016-0
ISBN:	9783031280160

Machine learning and optimization techniques for automotive cyber-physical systems
Machine learning and optimization techniques for automotive cyber-physical systems[electronic resource] /edited by Vipin Kumar Kukkala, Sudeep Pasricha. - Cham :Springer International Publishing :2023. - xv, 789 p. :ill., digital ;24 cm.

Chapter 1 Reliable Real-time Message Scheduling in Automotive Cyber-Physical Systems -- Chapter 2 Evolvement of Scheduling Theories for Autonomous Vehicles -- Chapter 3 Distributed Coordination and Centralized Scheduling for Automobiles at Intersections -- Chapter 4 Security Aware Design of Time-Critical Automotive Cyber-Physical Systems -- Chapter 5 Secure by Design Autonomous Emergency Braking Systems in Accordance with ISO 21434 -- Chapter 6 Resource Aware Synthesis of Automotive Security Primitives -- Chapter 7 Gradient-free Adversarial Attacks on 3D Point Clouds from LiDAR Sensors -- Chapter 8 Internet of Vehicles- Security and Research Road map -- Chapter 9 Protecting Automotive Controller Area Network: A Review on Intrusion Detection Methods Using Machine Learning Algorithms -- Chapter 10 Real-Time Intrusion Detection in Automotive Cyber-Physical Systems with Recurrent Autoencoders -- Chapter 11 Stacked LSTMs based Anomaly Detection in Time-Critical Automotive Networks -- Chapter 12 Deep AI for Anomaly Detection in Automotive Cyber-Physical Systems -- Chapter 13 Physical Layer Intrusion Detection and Localization on CAN bus -- Chapter 14 Spatiotemporal Information based Intrusion Detection Systems for In-vehicle Networks -- Chapter 15 In-Vehicle ECU Identification and Intrusion Detection from Electrical Signaling -- Chapter 16 Machine Learning for Security Resiliency in Connected Vehicle Applications -- Chapter 17 Object Detection in Autonomous Cyber-Physical Vehicle Platforms: Status and Open Challenges -- Chapter 18 Scene-Graph Embedding for Robust Autonomous Vehicle Perception -- Chapter 19 Sensing Optimization in Automotive Platforms -- Chapter 20 Unsupervised Random Forest Learning for Traffic Scenario Categorization -- Chapter 21 Development of Computer Vision Models for Drivable Region Detection in Snow Occluded Lane Lines -- Chapter 22 Machine Learning Based Perception Architecture Design for Semi-Autonomous Vehicles -- Chapter 23 -- Predictive Control During Acceleration Events to Improve Fuel Economy -- Chapter 24 Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic -- Chapter 25 Evaluation of Autonomous Vehicle Control Strategies Using Resilience Engineering -- Chapter 26 Safety-assured Design and Adaptation of Connected and Autonomous Vehicles -- Chapter 27 Identifying and Assessing Research Gaps for Energy Efficient Control of Electrified Autonomous Vehicle Eco-driving.

This book provides comprehensive coverage of various solutions that address issues related to real-time performance, security, and robustness in emerging automotive platforms. The authors discuss recent advances towards the goal of enabling reliable, secure, and robust, time-critical automotive cyber-physical systems, using advanced optimization and machine learning techniques. The focus is on presenting state-of-the-art solutions to various challenges including real-time data scheduling, secure communication within and outside the vehicle, tolerance to faults, optimizing the use of resource-constrained automotive ECUs, intrusion detection, and developing robust perception and control techniques for increasingly autonomous vehicles. The book describes state-of-the-art solutions to design secure, robust, and time-critical automotive systems; Various approaches are discussed that will impact the design of emerging autonomous vehicle systems; The content is relevant to researchers and industry practitioners interested in future automotive platforms.

ISBN: 9783031280160

Standard No.: 10.1007/978-3-031-28016-0doiSubjects--Topical Terms:

1387591
Cyber-Physical Systems.

LC Class. No.: TK7895.E42 / M33 2023

Dewey Class. No.: 006.22

Machine learning and optimization techniques for automotive cyber-physical systems
LDR:04589nam a2200325 a 4500 001 1116154
003 DE-He213
005 20230901153911.0
006 m d
007 cr nn 008maaau
008 240123s2023 sz s 0 eng d
020 $a 9783031280160 $q (electronic bk.)
020 $a 9783031280153 $q (paper)
024 7 $a 10.1007/978-3-031-28016-0 $2 doi
035 $a 978-3-031-28016-0
040 $a GP $c GP
041 0 $a eng
050 4 $a TK7895.E42 $b M33 2023
072 7 $a UKM $2 bicssc
072 7 $a TEC008010 $2 bisacsh
072 7 $a UKM $2 thema
082 0 4 $a 006.22 $2 23
090 $a TK7895.E42 $b M149 2023
245 0 0 $a Machine learning and optimization techniques for automotive cyber-physical systems $h [electronic resource] / $c edited by Vipin Kumar Kukkala, Sudeep Pasricha.
260 $a Cham : $c 2023. $b Springer International Publishing : $b Imprint: Springer,
300 $a xv, 789 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1 Reliable Real-time Message Scheduling in Automotive Cyber-Physical Systems -- Chapter 2 Evolvement of Scheduling Theories for Autonomous Vehicles -- Chapter 3 Distributed Coordination and Centralized Scheduling for Automobiles at Intersections -- Chapter 4 Security Aware Design of Time-Critical Automotive Cyber-Physical Systems -- Chapter 5 Secure by Design Autonomous Emergency Braking Systems in Accordance with ISO 21434 -- Chapter 6 Resource Aware Synthesis of Automotive Security Primitives -- Chapter 7 Gradient-free Adversarial Attacks on 3D Point Clouds from LiDAR Sensors -- Chapter 8 Internet of Vehicles- Security and Research Road map -- Chapter 9 Protecting Automotive Controller Area Network: A Review on Intrusion Detection Methods Using Machine Learning Algorithms -- Chapter 10 Real-Time Intrusion Detection in Automotive Cyber-Physical Systems with Recurrent Autoencoders -- Chapter 11 Stacked LSTMs based Anomaly Detection in Time-Critical Automotive Networks -- Chapter 12 Deep AI for Anomaly Detection in Automotive Cyber-Physical Systems -- Chapter 13 Physical Layer Intrusion Detection and Localization on CAN bus -- Chapter 14 Spatiotemporal Information based Intrusion Detection Systems for In-vehicle Networks -- Chapter 15 In-Vehicle ECU Identification and Intrusion Detection from Electrical Signaling -- Chapter 16 Machine Learning for Security Resiliency in Connected Vehicle Applications -- Chapter 17 Object Detection in Autonomous Cyber-Physical Vehicle Platforms: Status and Open Challenges -- Chapter 18 Scene-Graph Embedding for Robust Autonomous Vehicle Perception -- Chapter 19 Sensing Optimization in Automotive Platforms -- Chapter 20 Unsupervised Random Forest Learning for Traffic Scenario Categorization -- Chapter 21 Development of Computer Vision Models for Drivable Region Detection in Snow Occluded Lane Lines -- Chapter 22 Machine Learning Based Perception Architecture Design for Semi-Autonomous Vehicles -- Chapter 23 -- Predictive Control During Acceleration Events to Improve Fuel Economy -- Chapter 24 Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic -- Chapter 25 Evaluation of Autonomous Vehicle Control Strategies Using Resilience Engineering -- Chapter 26 Safety-assured Design and Adaptation of Connected and Autonomous Vehicles -- Chapter 27 Identifying and Assessing Research Gaps for Energy Efficient Control of Electrified Autonomous Vehicle Eco-driving.
520 $a This book provides comprehensive coverage of various solutions that address issues related to real-time performance, security, and robustness in emerging automotive platforms. The authors discuss recent advances towards the goal of enabling reliable, secure, and robust, time-critical automotive cyber-physical systems, using advanced optimization and machine learning techniques. The focus is on presenting state-of-the-art solutions to various challenges including real-time data scheduling, secure communication within and outside the vehicle, tolerance to faults, optimizing the use of resource-constrained automotive ECUs, intrusion detection, and developing robust perception and control techniques for increasingly autonomous vehicles. The book describes state-of-the-art solutions to design secure, robust, and time-critical automotive systems; Various approaches are discussed that will impact the design of emerging autonomous vehicle systems; The content is relevant to researchers and industry practitioners interested in future automotive platforms.
650 2 4 $a Cyber-Physical Systems. $3 1387591
650 2 4 $a Electronics Design and Verification. $3 1387809
650 1 4 $a Embedded Systems. $3 1026431
650 0 $a Automated vehicles. $3 1229151
650 0 $a Machine learning. $3 561253
650 0 $a Cooperating objects (Computer systems) $3 1020492
700 1 $a Pasricha, Sudeep. $3 655373
700 1 $a Kukkala, Vipin Kumar. $3 1429239
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-28016-0
950 $a Engineering (SpringerNature-11647)