A survey on trajectory-prediction methods for autonomous driving
In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
V Bharilya, N Kumar - Vehicular Communications, 2024 - Elsevier
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 …
Energy-based legged robots terrain traversability modeling via deep inverse reinforcement learning
This work reports ondeveloping a deep inverse reinforcement learning method for legged
robots terrain traversability modeling that incorporates both exteroceptive and proprioceptive …
robots terrain traversability modeling that incorporates both exteroceptive and proprioceptive …
Personalized route recommendation for ride-hailing with deep inverse reinforcement learning and real-time traffic conditions
Personalized route recommendation aims to recommend routes based on users' route
preference. The vast amount of GPS trajectories tracking driving behavior has made deep …
preference. The vast amount of GPS trajectories tracking driving behavior has made deep …
AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning
Taxi cruising route planning has attracted considerable attention, and relevant studies can
be broadly categorized into three main streams: recommending one or multiple areas …
be broadly categorized into three main streams: recommending one or multiple areas …
Spatiotemporal costmap inference for MPC via deep inverse reinforcement learning
It can be difficult to autonomously produce driver behavior so that it appears natural to other
traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this …
traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this …
[HTML][HTML] Pyramidal 3D feature fusion on polar grids for fast and robust traversability analysis on CPU
Self-driving vehicles and autonomous ground robots require a reliable and accurate method
to analyze the traversability of the surrounding environment for safe navigation. This paper …
to analyze the traversability of the surrounding environment for safe navigation. This paper …
Anomalous ride-hailing driver detection with deep transfer inverse reinforcement learning
The rapid expansion in group size of online ride-hailing drivers has made anomalous driver
detection become a critical issue, which substantially affects the safety and operation …
detection become a critical issue, which substantially affects the safety and operation …
Pushing the limits of learning-based traversability analysis for autonomous driving on cpu
Self-driving vehicles and autonomous ground robots require a reliable and accurate method
to analyze the traversability of the surrounding environment for safe navigation. This paper …
to analyze the traversability of the surrounding environment for safe navigation. This paper …
An intelligent TCP congestion control method based on deep Q network
Y Wang, L Wang, X Dong - Future Internet, 2021 - mdpi.com
To optimize the data migration performance between different supercomputing centers in
China, we present TCP-DQN, which is an intelligent TCP congestion control method based …
China, we present TCP-DQN, which is an intelligent TCP congestion control method based …