Vipin Kumar Kukkala,
Sudeep Pasricha
Springer International Publishing
e druk, 2023
9783031280153
Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems
Specificaties
Gebonden, blz.
|
Engels
Springer International Publishing |
e druk, 2023
ISBN13: 9783031280153
Rubricering
Verwachte levertijd ongeveer 9 werkdagen
Samenvatting
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.
Specificaties
ISBN13:9783031280153
Taal:Engels
Bindwijze:gebonden
Uitgever:Springer International Publishing
Inhoudsopgave
<p>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.</p>