A review of safe reinforcement learning: Methods, theory and applications
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …
making tasks. However, safety concerns are raised during deploying RL in real-world …
A survey of collaborative machine learning using 5G vehicular communications
SV Balkus, H Wang, BD Cornet… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
By enabling autonomous vehicles (AVs) to share data while driving, 5G vehicular
communications allow AVs to collaborate on solving common autonomous driving tasks …
communications allow AVs to collaborate on solving common autonomous driving tasks …
End-to-end autonomous driving: Challenges and frontiers
The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
Dense reinforcement learning for safety validation of autonomous vehicles
One critical bottleneck that impedes the development and deployment of autonomous
vehicles is the prohibitively high economic and time costs required to validate their safety in …
vehicles is the prohibitively high economic and time costs required to validate their safety in …
A digital twin smart city for citizen feedback
A digital twin is a digital representation of a physical process, person, place, system or
device. Digital twins were originally designed to improve manufacturing processes using …
device. Digital twins were originally designed to improve manufacturing processes using …
Learning from all vehicles
D Chen, P Krähenbühl - … of the IEEE/CVF Conference on …, 2022 - openaccess.thecvf.com
In this paper, we present a system to train driving policies from experiences collected not just
from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of …
from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of …
Trace and pace: Controllable pedestrian animation via guided trajectory diffusion
We introduce a method for generating realistic pedestrian trajectories and full-body
animations that can be controlled to meet user-defined goals. We draw on recent advances …
animations that can be controlled to meet user-defined goals. We draw on recent advances …
Deep reinforcement learning for autonomous driving: A survey
With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …
(RL) has become a powerful learning framework now capable of learning complex policies …
Transportation 5.0: The DAO to safe, secure, and sustainable intelligent transportation systems
In 2014, IEEE Intelligent Transportation Systems Society established a Technical Committee
on Transportation 5.0 with the mission of promoting and transforming the deployment of …
on Transportation 5.0 with the mission of promoting and transforming the deployment of …
Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning
Driving safely requires multiple capabilities from human and intelligent agents, such as the
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …