A Multi-Task Reinforcement Learning Approach for Navigating Unsignalized Intersections

Kai, Shixiong; Wang, Bin; Chen, Dong; Hao, Jianye; Liu, Wulong

doi:10.1109/iv47402.2020.9304542

Cited by 31 publications

(24 citation statements)

References 11 publications

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“…It gains significant quantitative improvements compared with DQN baseline. In [10], unsignalized intersection navigation task is modeled as a multi-task RL problem, in which turning left, turning right, and going straight are considered as specific sub-tasks. Through a multi-task learning framework, the agent learns to handle three navigating tasks at the same time and shows a competitive performance with single-task agents.…”

Section: Intersection Navigationmentioning

confidence: 99%

“…An optimal correction value of original dangerous action will help with the enhancement in safety without losing much efficiency. In this paper, the assumption that ego vehicle in unsignalized intersections has motion planning information will be hold similarly with [10,11]. We aim to investigate the multi-task unsignalized intersection navigation problem in dense traffic including turning left, going straight, and turning right.…”

Section: Introductionmentioning

confidence: 99%

“…1. Compared with the state of the art (SOTA) work [10], the designed safety layer model and actor-critic with social attention can improve negotiation skills of the ego vehicle when interacting with other traffic participants. As for the original contributions, this paper:…”

Section: Introductionmentioning

confidence: 99%

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Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic

Liu¹,

Zhang²,

Zhao³

2022

Preprint

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Multi-task intersection navigation including the unprotected turning left, turning right, and going straight in dense traffic is still a challenging task for autonomous driving. For the human driver, the negotiation skill with other interactive vehicles is the key to guarantee safety and efficiency. However, it is hard to balance the safety and efficiency of the autonomous vehicle for multi-task intersection navigation. In this paper, we formulate a multi-task safe reinforcement learning with social attention to improve the safety and efficiency when interacting with other traffic participants. Specifically, the social attention module is used to focus on the states of negotiation vehicles. In addition, a safety layer is added to the multi-task reinforcement learning framework to guarantee safe negotiation. We compare the experiments in the simulator SUMO with abundant traffic flows and CARLA with high-fidelity vehicle models, which both show that the proposed algorithm can improve safety with consistent traffic efficiency for multi-task intersection navigation.

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Section: Intersection Navigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic

Liu¹,

Zhang²,

Zhao³

2022

Preprint

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“…In Table I, we compare our approach with the current stateof-the-art in navigating unsignaled intersections, roundabouts, and merging scenarios on the basis of optimality guarantees, multi-agent versus single-agent planning (MAP), description of action space (AS), incentive compatibility (IC), and realworld applicability. DRL-based methods [2], [19], [20], [25], [26] learn a navigation policy using the notion of expected reward received by an agent from taking a particular action in a particular state. This policy is learned from trajectories obtained via traffic simulators using Q-learning [27] and is very hard as well as expensive to train.…”

Section: Prior Workmentioning

confidence: 99%

“…Capasso et al [2] use additional signals such as traffic signs (stop, yield, none) to regulate the movement and actions of other agents. In terms of real world applications, Kai et al [20] learn a unified policy for multiple tasks and also demonstrate their approach on a real robot.…”

Section: Prior Workmentioning

confidence: 99%

GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging

Chandra¹,

Manocha²

2021

Preprint

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We present a new method for multi-agent planning involving human drivers and autonomous vehicles (AVs) in unsignaled intersections, roundabouts, and during merging. In multi-agent planning, the main challenge is to predict the actions of other agents, especially human drivers, as their intentions are hidden from other agents. Our algorithm uses game theory to develop a new auction, called GAMEPLAN, that directly determines the optimal action for each agent based on their driving style (which is observable via commonly available sensors like lidars and cameras). GAMEPLAN assigns a higher priority to more aggressive or impatient drivers and a lower priority to more conservative or patient drivers; we theoretically prove that such an approach, although counter-intuitive, is gametheoretically optimal. Our approach successfully prevents collisions and deadlocks. We compare our approach with prior state-of-the-art auction techniques including economic auctions, time-based auctions (first-in first-out), and random bidding and show that each of these methods result in collisions among agents when taking into account driver behavior. We additionally compare with methods based on deep reinforcement learning, deep learning, and game theory and present our benefits over these approaches. Finally, we show that our approach can be implemented in the real-world with human drivers.

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Deep reinforcement learning for autonomous driving in uncontrolled intersections of Indian roads

Shankar,

Mittur,

Narasimhan

et al. 2024

Multimed Tools Appl

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A Multi-Task Reinforcement Learning Approach for Navigating Unsignalized Intersections

Cited by 31 publications

References 11 publications

Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic

Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic

GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging

Deep reinforcement learning for autonomous driving in uncontrolled intersections of Indian roads

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