Interactive autonomous driving is an evolving research domain that demands an autonomous vehicle(AV)to exhibit adaptability to new environments,cognizance of surrounding traffic conditions,and proficient decision-maki...Interactive autonomous driving is an evolving research domain that demands an autonomous vehicle(AV)to exhibit adaptability to new environments,cognizance of surrounding traffic conditions,and proficient decision-making ability in complex human-dominated scenarios to guarantee safe navigation and promote social compatibility.This paper reviews the diverse methodologies utilized in interactive driving for AVs.Various techniques will be investigated for their unique contributions and capabilities in developing AV systems,such as long short-term memory(LSTM),transformer,artificial potential field(APF),game theory,reinforcement learning(RL)/deep reinforcement learning(DRL),and partially observable Markov decision processes(POMDP),among others.Recent advancements based on these methodologies are summarized to elucidate their application rationale in interactive driving scenarios.The strengths and challenges inherent to each approach within the context of interactive driving are further assessed.Additionally,the resolution of these challenges is explored through integrating different methods.Therefore,a comparative analysis offers crucial perspectives for advancing autonomous driving technologies.This review exclusively focuses on the interactions between AVs and human-driven vehicles(HDVs).展开更多
Addressing optimal confrontation methods in multi-agent attack-defense scenarios is a complex challenge.Multi-Agent Reinforcement Learning(MARL)provides an effective framework for tackling sequential decision-making p...Addressing optimal confrontation methods in multi-agent attack-defense scenarios is a complex challenge.Multi-Agent Reinforcement Learning(MARL)provides an effective framework for tackling sequential decision-making problems,significantly enhancing swarm intelligence in maneuvering.However,applying MARL to unmanned swarms presents two primary challenges.First,defensive agents must balance autonomy with collaboration under limited perception while coordinating against adversaries.Second,current algorithms aim to maximize global or individual rewards,making them sensitive to fluctuations in enemy strategies and environmental changes,especially when rewards are sparse.To tackle these issues,we propose an algorithm of MultiAgent Reinforcement Learning with Layered Autonomy and Collaboration(MARL-LAC)for collaborative confrontations.This algorithm integrates dual twin Critics to mitigate the high variance associated with policy gradients.Furthermore,MARL-LAC employs layered autonomy and collaboration to address multi-objective problems,specifically learning a global reward function for the swarm alongside local reward functions for individual defensive agents.Experimental results demonstrate that MARL-LAC enhances decision-making and collaborative behaviors among agents,outperforming the existing algorithms and emphasizing the importance of layered autonomy and collaboration in multi-agent systems.The observed adversarial behaviors demonstrate that agents using MARL-LAC effectively maintain cohesive formations that conceal their intentions by confusing the offensive agent while successfully encircling the target.展开更多
基金partially supported by the Texas Tech University Graduate School Fellowship.
文摘Interactive autonomous driving is an evolving research domain that demands an autonomous vehicle(AV)to exhibit adaptability to new environments,cognizance of surrounding traffic conditions,and proficient decision-making ability in complex human-dominated scenarios to guarantee safe navigation and promote social compatibility.This paper reviews the diverse methodologies utilized in interactive driving for AVs.Various techniques will be investigated for their unique contributions and capabilities in developing AV systems,such as long short-term memory(LSTM),transformer,artificial potential field(APF),game theory,reinforcement learning(RL)/deep reinforcement learning(DRL),and partially observable Markov decision processes(POMDP),among others.Recent advancements based on these methodologies are summarized to elucidate their application rationale in interactive driving scenarios.The strengths and challenges inherent to each approach within the context of interactive driving are further assessed.Additionally,the resolution of these challenges is explored through integrating different methods.Therefore,a comparative analysis offers crucial perspectives for advancing autonomous driving technologies.This review exclusively focuses on the interactions between AVs and human-driven vehicles(HDVs).
基金co-supported by the National Natural Science Foundation of China(Nos.72371052 and 71871042).
文摘Addressing optimal confrontation methods in multi-agent attack-defense scenarios is a complex challenge.Multi-Agent Reinforcement Learning(MARL)provides an effective framework for tackling sequential decision-making problems,significantly enhancing swarm intelligence in maneuvering.However,applying MARL to unmanned swarms presents two primary challenges.First,defensive agents must balance autonomy with collaboration under limited perception while coordinating against adversaries.Second,current algorithms aim to maximize global or individual rewards,making them sensitive to fluctuations in enemy strategies and environmental changes,especially when rewards are sparse.To tackle these issues,we propose an algorithm of MultiAgent Reinforcement Learning with Layered Autonomy and Collaboration(MARL-LAC)for collaborative confrontations.This algorithm integrates dual twin Critics to mitigate the high variance associated with policy gradients.Furthermore,MARL-LAC employs layered autonomy and collaboration to address multi-objective problems,specifically learning a global reward function for the swarm alongside local reward functions for individual defensive agents.Experimental results demonstrate that MARL-LAC enhances decision-making and collaborative behaviors among agents,outperforming the existing algorithms and emphasizing the importance of layered autonomy and collaboration in multi-agent systems.The observed adversarial behaviors demonstrate that agents using MARL-LAC effectively maintain cohesive formations that conceal their intentions by confusing the offensive agent while successfully encircling the target.
