Autonomous driving in complex traffic scenarios is a vital challenge, and deep reinforcement learning (DRL) has been extensively applied to address this issue. The recent advancement of vehicle-to-everything (V2X) tec...
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Autonomous driving in complex traffic scenarios is a vital challenge, and deep reinforcement learning (DRL) has been extensively applied to address this issue. The recent advancement of vehicle-to-everything (V2X) technology has provided abundant perceptual information for DRL agents, improving the accuracy and safety of decisioncontrol. However, existing research on green wave traffic scenes has difficulty adapting to multi signal scenarios with single-signal countdown models, which lack complete signal state information. To address this limitation, an enhanced DRL model with bird's eye view (BEV) design strategy is proposed for vehicle-road collaborative autonomous driving scenarios. The constructed model introduces a state prediction fusion strategy to compensate for state information. Specifically, state information is first predicted by fusing perception results from vehicles and roadside units (RSUs) at different moments. Then, the recommended velocity is derived for green wave passage, called the green wave velocity belt, and incorporate it into the state space as two variables in the state vector. Finally, a relevant reward term in the reward function is designed to guide agent learning strategies. The proposed method is trained on the basis of the parallel DreamerV3 framework. The results show that the proposed approach can effectively integrate multi-source perceptual information, improving training efficiency and control performance, and demonstrating great effectiveness and practical application value.
Ramp merging areas on highways often serve as bottleneck areas, leading to frequent interactions and accidents between vehicles on the ramp and the arterial road. This results in severe congestion and reduced traffic ...
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ISBN:
(纸本)9783031705069;9783031705076
Ramp merging areas on highways often serve as bottleneck areas, leading to frequent interactions and accidents between vehicles on the ramp and the arterial road. This results in severe congestion and reduced traffic performance. The emergence of Connected Autonomous Vehicles (CAVs) offers advanced solutions to address these issues and improve traffic operations at ramp merging areas. While previous studies have explored CAV decision-making approaches such as optimization control, model predictive control, and reinforcement learning, they face difficulties in accurately modeling the complex and dynamic scenarios of ramp merging. To overcome these challenges, this paper proposes a collaborative decision-making and control model based on Multi-agent Reinforcement Learning (MARL) for mixed vehicles (CAV-HDV) in multi-lane ramp merging scenarios on arterial roads. The paper introduces three novel MARL algorithms and conducts simulations in six different scenarios to evaluate traffic performance under various lane numbers and traffic densities. The results demonstrate the effectiveness of the proposed collaborative model for ramp merging vehicles. The proposed algorithms significantly reduce collision rates and improve traffic efficiency.
This paper focuses on the problem of decision-making and control in an autonomous driving application for highways. By considering the decision-making and control problem as an obstacle avoidance path planning problem...
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This paper focuses on the problem of decision-making and control in an autonomous driving application for highways. By considering the decision-making and control problem as an obstacle avoidance path planning problem, the paper proposes a novel approach to path planning, which exploits the structured environment of one-way roads. As such, the obstacle avoidance path planning problem is formulated as a convex optimization problem within a receding horizon control framework, as the minimization of the deviation from a desired velocity and lane, subject to a set of constraints introduced to avoid collision with surrounding vehicles, stay within the road boundaries, and abide the physical limitations of the vehicle dynamics. The ability of the proposed approach to generate appropriate traffic dependent maneuvers is demonstrated in simulations concerning traffic scenarios on a two-lane, one-way road with one and two surrounding vehicles. (C) 2015 Elsevier Ltd. All rights reserved.
Networked sensing and control has attracted significant interest in recent years due to its wide applications. For example, sensor networks, especially wireless sensor networks, have found important applications in en...
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Networked sensing and control has attracted significant interest in recent years due to its wide applications. For example, sensor networks, especially wireless sensor networks, have found important applications in environmental monitoring, agriculture, building and industrial automation, machine condition monitoring, intelligent transportation systems, health care, surveillance, and defense. On the other hand, due to the flexibility and significant COSt-saving,
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