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 …
Advancements in humanoid robots: A comprehensive review and future prospects
This paper provides a comprehensive review of the current status, advancements, and future
prospects of humanoid robots, highlighting their significance in driving the evolution of next …
prospects of humanoid robots, highlighting their significance in driving the evolution of next …
When demonstrations meet generative world models: A maximum likelihood framework for offline inverse reinforcement learning
Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards
and environment dynamics that underlie observed actions in a fixed, finite set of …
and environment dynamics that underlie observed actions in a fixed, finite set of …
Blending data-driven priors in dynamic games
As intelligent robots like autonomous vehicles become increasingly deployed in the
presence of people, the extent to which these systems should leverage model-based game …
presence of people, the extent to which these systems should leverage model-based game …
Robot skill learning and the data dilemma it faces: a systematic review
Purpose Compared with traditional methods relying on manual teaching or system
modeling, data-driven learning methods, such as deep reinforcement learning and imitation …
modeling, data-driven learning methods, such as deep reinforcement learning and imitation …
Local Trajectory Planning for Obstacle Avoidance of Unmanned Tracked Vehicles Based on Artificial Potential Field Method
L Zhai, C Liu, X Zhang, C Wang - IEEE Access, 2024 - ieeexplore.ieee.org
A trajectory planning method for local obstacle avoidance based on an improved artificial
potential field (APF) method is proposed, which is aimed at the problem for dual motor …
potential field (APF) method is proposed, which is aimed at the problem for dual motor …
[HTML][HTML] A physics-informed Bayesian optimization method for rapid development of electrical machines
Advanced slot and winding designs are imperative to create future high performance
electrical machines (EM). As a result, the development of methods to design and improve …
electrical machines (EM). As a result, the development of methods to design and improve …
Maximum Entropy Inverse Reinforcement Learning Using Monte Carlo Tree Search for Autonomous Driving
JAR da Silva, V Grassi, DF Wolf - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Autonomous vehicles must be capable of driving safely and having some level of social
compliance with human drivers. Acting egoistically can make other drivers to take …
compliance with human drivers. Acting egoistically can make other drivers to take …
Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Towards Trustworthy, Interpretable, and Explainable …
This article presents a literature review of the past five years of studies using Deep
Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic …
Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic …
Understanding e-bicycle overtaking strategy: insights from inverse reinforcement learning modelling
L Yue, M Abdel-Aty, MH Zaki, O Zheng… - … A: transport science, 2024 - Taylor & Francis
Understanding e-bicycle overtaking strategies (ie e-bicycle overtakes bicycle/e-bicycle) is
important for developing bicycle simulation models and analyzing bicycle traffic. However …
important for developing bicycle simulation models and analyzing bicycle traffic. However …