Recent Advances in Autonomous Vehicle Technology―Perception and Path Planning (Sustainable Mobility & Energy) - Tapa dura

Acerca del autor

Daniel Watzenig, born in Austria, holds a PhD in electrical engineering and habilitation from Graz University of Technology, where he is Full Professor of Multi-Sensor Perception of Autonomous Systems. He is CTO at Virtual Vehicle Research, Graz. His work focuses on robotics, sensor fusion, continual learning, and decision-making under uncertainty. Author of 200+ publications, he is Editor-in-Chief of the SAE JCAV, guest lecturer at Stanford and Tongji Universities, founder of Autonomous Racing Graz Team, IEEE Austria Vice Chair, and defense robotics consultant to Austria’s military.

De la contraportada

In autonomous vehicles, perception and planning are core systems enabling safe, efficient navigation. Perception interprets surroundings using data from sensors like cameras, radar, and LiDAR to detect and classify objects, traffic signs, road boundaries, and environmental conditions. Advances in machine learning and computer vision continue to improve perception accuracy and reliability. Planning determines the vehicle’s actions via route, behavioral, and motion planning to safely reach its destination. Seamless integration of perception and planning is essential, relying on fast, reliable data exchange through low-latency, high-dependability frameworks. This book presents the latest technologies in perception and planning, emphasizing their integration and highlighting innovations. It serves researchers, students, engineers, ICT professionals, and industry leaders alike.

The Content

• Scenario-based quantification of the impact of automated vehicles on traffic
• Adversial examples in environment perception for automated driving
• Stable resampling strategies for radar-based dynamic occupancy grids
• Local point cloud features for LiDAR self-supervised representation learning
• On torque-vectoring control for the obstacle avoidance scenario
• Performance evaluation of path tracking controllers for scaled robotic cars
• A nonlinear dead-time compensation method for path tracking control

The Editor

Daniel Watzenig, born in Austria, holds a PhD in electrical engineering and habilitation from Graz University of Technology, where he is Full Professor of Multi-Sensor Perception of Autonomous Systems. He is CTO at Virtual Vehicle Research, Graz. His work focuses on robotics, sensor fusion, continual learning, and decision-making under uncertainty. Author of 200+ publications, he is Editor-in-Chief of the SAE JCAV, guest lecturer at Stanford and Tongji Universities, founder of Autonomous Racing Graz Team, IEEE Austria Vice Chair, and defense robotics consultant to Austria’s military.

The Book Series

Sustainable Mobility and Energy highlights the crucial role of sustainable mobility in reducing CO2 emissions. This series features insights from leading experts on electric and fuel cell vehicles, which offer eco-friendly alternatives to conventional engines, especially when powered by renewable energy sources. Utilizing clean energy significantly reduces air pollution and dependence on fossil fuels. Additionally, the integration of modern communication technology enhances traffic control, reduces congestion, and increases road safety. Join us in exploring innovative solutions for a greener and more efficient future.