SAE International Journal of Connected and Automated Vehicles
2021 Special Issue: Machine Learning and Deep Learning Techniques for Automotive Applications JRN-CA-SI-05
Volume 4, Issue 3, 2021
All articles in this special issue have been carefully selected to cover critical aspects of machine learning and deep learning techniques in the context of next-generation connected and automated vehicles.
Special Issue Co-Editors:Kanwar Bharat Singh, The Goodyear Tire & Rubber Company, Luxembourg
Mustafa Ali Arat, Flawless Photonics, Luxembourg
Michael Unterreiner, CARIAD, Volkswagen Group, Germany
The SAE International Journal of Connected and Automated Vehicles furthers the state of the art of engineering research by promoting high-quality theoretical and applied investigations in the arena of connected and autonomous vehicles (CAVs) in on-road, off-road, and aerial operational environments.
All articles in the special issue contribute to ongoing discussions concerning the increasing scale of data, computational power, and algorithmic innovations that drive the rapid strides in the field of machine learning (ML) and deep learning (DL) for automotive applications. Vehicles of the future will feature ML/DL-based systems that enhance comfort and safety through intelligent functions that ease the driver’s workload. Algorithmic innovations for CAVs will provide a unique customer experience while simultaneously delivering cost and revenue benefits to OEMs, suppliers, dealers, insurers, fleets, tech players, and beyond. This special issue highlights these themes.
Article Titles:
- Path Planning and Obstacle Avoidance for Automated Driving Systems using Rapidly-Exploring Random Tree Algorithm
- Clustering-Based Trajectory Prediction of Vehicles Interacting with Vulnerable Road Users
- Uncertainty Estimation for Neural Time Series with an Application to Side-Slip Angle Estimation
- Research on Vehicle Trajectory Prediction Method for Intersections without Signal Lights
- Prediction of Vehicle Cabin Occupant Thermal Comfort using Deep Learning and Computational Fluid Dynamics
- Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning
- Application of Neural Networks to External Parameter Estimation for Non-Linear Vehicle Models
