Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
Genad: Generative end-to-end autonomous driving
Directly producing planning results from raw sensors has been a long-desired solution for
autonomous driving and has attracted increasing attention recently. Most existing end-to …
autonomous driving and has attracted increasing attention recently. Most existing end-to …
Rethinking imitation-based planners for autonomous driving
In recent years, imitation-based driving planners have reported considerable success.
However, due to the absence of a standardized benchmark, the effectiveness of various …
However, due to the absence of a standardized benchmark, the effectiveness of various …
Velocity Field: An Informative Traveling Cost Representation for Trajectory Planning
Trajectory planning involves generating a series of space points to be followed in the near
future. However, due to the complex and uncertain nature of the driving environment, it is …
future. However, due to the complex and uncertain nature of the driving environment, it is …
A Survey of the State-of-the-Art Reinforcement Learning-Based Techniques for Autonomous Vehicle Trajectory Prediction
Autonomous Vehicles (AVs) have emerged as a promising solution by replacing human
drivers with advanced computer-aided decision-making systems. However, for AVs to …
drivers with advanced computer-aided decision-making systems. However, for AVs to …