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Path Planning and Tracking for Vehicle Collision Avoidance in Lateral and Longitudinal Motion Directions
In recent years, the control of Connected and Automated Vehicles (CAVs) has attracted strong attention for various automotive applications. One of the important features demanded of CAVs is collision avoidance, whether it is a stationary or a moving obstacle. Due to complex traffic conditions and various vehicle dynamics, the collision avoidance system should ensure that the vehicle can avoid collision with other vehicles or obstacles in longitudinal and lateral directions simultaneously. The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via Forward Collision Warning (FCW), Brake Assist System (BAS), and Autonomous Emergency Braking...
Exploring Alternative Energy Sources
"Exploring Alternative Energy Sources" is a comprehensive guide to understanding and embracing the vast array of alternative energy sources that hold the key to a greener and more sustainable future. We explore the diverse landscape of alternative energy, providing in-depth insights into renewable resources, cutting-edge technologies, and their transformative power. From the sun’s rays to the gentle breeze, from the depths of the oceans to the heart of the Earth, we delve into the promising realms of clean and efficient energy. Key Features: • Comprehensive Coverage: Solar power, wind energy, hydropower, geothermal systems, and more. • Emerging Technologies: Spotlighting the latest adv...
Towards Autonomous Robotic Systems
The volume LNAI 13054 constitutes the refereed proceedings of the 22th Annual Conference Towards Autonomous Robotic Systems, TAROS 2021, held in Lincoln, UK, in September 2021.*The 45 full papers were carefully reviewed and selected from 66 submissions. Organized in the topical sections "Algorithms" and "Systems", they discuss significant findings and advances in the following areas: artificial intelligence; mechatronics; image processing and computer vision; special purpose and application-based systems; user interfaces and human computer interaction. * The conference was held virtually due to the COVID-19 pandemic.
Trust Management in the Internet of Vehicles
The Internet of Vehicles (IoV) is referred to as an efficient and inevitable convergence of the Internet of Things, intelligent transportation systems, edge / fog and cloud computing, and big data, all of which could be intelligently harvested for the cooperative vehicular safety and non-safety applications as well as cooperative mobility management. A secure and low-latency communication is, therefore, indispensable to meet the stringent performance requirements of the safety-critical vehicular applications. Whilst the challenges surrounding low latency are being addressed by the researchers in both academia and industry, it is the security of an IoV network which is of paramount importance...
AI Ethics
This overview of the ethical issues raised by artificial intelligence moves beyond hype and nightmare scenarios to address concrete questions—offering a compelling, necessary read for our ChatGPT era. Artificial intelligence powers Google’s search engine, enables Facebook to target advertising, and allows Alexa and Siri to do their jobs. AI is also behind self-driving cars, predictive policing, and autonomous weapons that can kill without human intervention. These and other AI applications raise complex ethical issues that are the subject of ongoing debate. This volume in the MIT Press Essential Knowledge series offers an accessible synthesis of these issues. Written by a philosopher of ...
Deep Learning for Autonomous Vehicle Control
The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.
