Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning
In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …
vehicles plan future trajectories. We develop a partially observable Markov decision process …
Impact of autonomous vehicles on the car-following behavior of human drivers
The past few years have been witness to an increase in autonomous vehicle (AV)
development and testing. However, even with a fully developed and comprehensively tested …
development and testing. However, even with a fully developed and comprehensively tested …
Spatial-Temporal-Spectral LSTM: A Transferable Model for Pedestrian Trajectory Prediction
Predicting the trajectories of pedestrians is critical for developing safe advanced driver
assistance systems and autonomous driving systems. Most existing models for pedestrian …
assistance systems and autonomous driving systems. Most existing models for pedestrian …
Rs2g: Data-driven scene-graph extraction and embedding for robust autonomous perception and scenario understanding
Effectively capturing intricate interactions among road users is of critical importance to
achieving safe navigation for autonomous vehicles. While graph learning (GL) has emerged …
achieving safe navigation for autonomous vehicles. While graph learning (GL) has emerged …
[HTML][HTML] Impact of vehicle arrangement in mixed autonomy traffic on emissions
The environmental impact of the driving behaviour of autonomous vehicles (AVs) is not yet
well-understood due to the scarcity of empirical mixed autonomy trajectory data. This study …
well-understood due to the scarcity of empirical mixed autonomy trajectory data. This study …
RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding
Effectively capturing intricate interactions among road users is of critical importance to
achieving safe navigation for autonomous vehicles. While graph learning (GL) has emerged …
achieving safe navigation for autonomous vehicles. While graph learning (GL) has emerged …
Conditional Variational Autoencoder Networks for Autonomous Vehicle Path Prediction
Mobility of autonomous vehicles is a challenging task to implement. Under the given traffic
circumstances, all agent vehicles' behavior is to be understood and their paths for a short …
circumstances, all agent vehicles' behavior is to be understood and their paths for a short …
Safe and secure design of connected and autonomous vehicles
X Liu - 2023 - search.proquest.com
Abstract Machine learning-based techniques have shown great promises in perception,
prediction, planning, and general decision-making for improving task performance of …
prediction, planning, and general decision-making for improving task performance of …
The Environmental Impact of Connected and Automated Vehicles' Car-following Behavior
A Alhariqi - 2023 - unsworks.unsw.edu.au
This thesis focuses on the environmental impact of car-following (CF) driving behavior in
mixed autonomy traffic. Given the shortage of real-world mixed autonomy trajectory data, this …
mixed autonomy traffic. Given the shortage of real-world mixed autonomy trajectory data, this …
A Predictive-Prescriptive Safety Framework at Intersections in a Connected Vehicle Environment
E Zhang - 2022 - deepblue.lib.umich.edu
The connected and automated vehicle (CAV) technology in recent years has demonstrated
its potential in improving efficiency in transportation systems. Prediction, as a key component …
its potential in improving efficiency in transportation systems. Prediction, as a key component …