Safe Reinforcement Learning on Autonomous Vehicles

Isele, David; Nakhaei, Alireza; Fujimura, Kikuo

doi:10.1109/iros.2018.8593420

Cited by 56 publications

(53 citation statements)

References 25 publications

(25 reference statements)

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“…4) Safe (deep) reinforcement learning: Ultimately, research evolved in a novel direction on exploring safe (deep) RL techniques. The scholars in [27] investigated how to use prediction for safe learning. Addressing the challenges of existing RB and RL approaches, a modular decision making algorithm was proposed in [28].…”

Section: Related Workmentioning

confidence: 99%

A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms

Aksjonov¹,

Kyrki²

2022

Preprint

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While artificial-intelligence-based methods suffer from lack of transparency, rule-based methods dominate in safety-critical systems. Yet, the latter cannot compete with the first ones in robustness to multiple requirements, for instance, simultaneously addressing safety, comfort, and efficiency. Hence, to benefit from both methods they must be joined in a single system. This paper proposes a decision making and control framework, which profits from advantages of both the rule-and machine-learning-based techniques while compensating for their disadvantages. The proposed method embodies two controllers operating in parallel, called Safety and Learned. A rule-based switching logic selects one of the actions transmitted from both controllers. The Safety controller is prioritized every time, when the Learned one does not meet the safety constraint, and also directly participates in the safe Learned controller training. Decision making and control in autonomous driving is chosen as the system case study, where an autonomous vehicle learns a multi-task policy to safely cross an unprotected intersection. Multiple requirements (i.e., safety, efficiency, and comfort) are set for vehicle operation. A numerical simulation is performed for the proposed framework validation, where its ability to satisfy the requirements and robustness to changing environment is successfully demonstrated.

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Section: Related Workmentioning

confidence: 99%

A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms

Aksjonov¹,

Kyrki²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The proposed algorithm combines multimodal pedestrian trajectory forecasting and vehicle kinematic constraints to ensure smooth pedestrian-vehicle interactions, efficient operation, and safety. Deep Q-learning networks (DQNs) [26] are also proposed to learn policies that optimize intelligent vehicle intersection handling. They have two goals in mind.…”

Section: Reinforcement Learning (Rl) Modelmentioning

confidence: 99%

Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review

Kolekar

Gite²,

Pradhan

et al. 2021

IEEE Access

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Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence (AI) techniques.Accurate behavior prediction of surrounding traffic actors is essential for the safe and secure navigation of the intelligent vehicle. Minor misbehavior of these vehicles on the busy roads may lead to an accident. Due to this, there is a need for vehicle behavior research work in today's era. This research article reviews traffic actors' behavior prediction techniques for intelligent vehicles to perceive, infer, and anticipate other vehicles' intentions and future actions. It identifies the key strategies and methods for AI, emerging trends, datasets, and ongoing research issues in these fields. As per the authors' knowledge, this is the first systematic literature review dedicated to the vehicle behavior study examining existing academic literature published by peer review venues between 2011 and 2021. A systematic review was undertaken to examine these papers, and five primary research questions have been addressed. The findings show that using sophisticated input representation that includes traffic rules and road geometry, artificial intelligence-based solutions applied to behavior prediction of traffic actors for intelligent vehicles have shown promising success, particularly in complex driving scenarios. Finally, the paper summarizes the most widely used approaches in behavior prediction of traffic actors for intelligent vehicles, which the authors believe serves as a foundation for future research in behavior prediction of surrounding traffic actors for secure and accurate intelligent vehicle navigation. INDEX TERMS intelligent driving; deep learning; intelligent vehicles; vehicle behavior prediction; pedestrian behavior prediction. Inclusion criteriaResearch articles must be original research articles instead of review/survey articles. Research articles that are published between2011 and 2021.Research articles that answer at least one research question. Research articles must contain search keywords either in the title, abstract, or full text. The proposed solution in the research article must be evaluated. The solution developed must be aimed to solve problems on behavior prediction of traffic actors. Exclusion criteriaNon-English Research articles Duplicate research articles Research articles with non-availability of full text Research articles that are not relevant to pedestrians and vehicle behavior prediction concerning intelligent vehicle III. RESULTSThis section summarizes the findings of systematic analysis. It answers the research questions posed above based on the results of this review process.

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“…Several strategies have been proposed to make solving these problems more efficient. These techniques include making assumptions that restrict branching such as using klevel reasoning [19], assuming simplified models of other agents [20], and using temporarily extended actions to reduce the required depth of a search [21]. There are other works in autonomous driving literature which also use game theoretic strategies, and like our work, they adopt some of these approximations.…”

Section: Davidmentioning

confidence: 99%

Interactive Decision Making for Autonomous Vehicles in Dense Traffic

Isele

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Dense urban traffic environments can produce situations where accurate prediction and dynamic models are insufficient for successful autonomous vehicle motion planning. We investigate how an autonomous agent can safely negotiate with other traffic participants, enabling the agent to handle potential deadlocks. Specifically we consider merges where the gap between cars is smaller than the size of the ego vehicle. We propose a game theoretic framework capable of generating and responding to interactive behaviors. Our main contribution is to show how game-tree decision making can be executed by an autonomous vehicle, including approximations and reasoning that make the tree-search computationally tractable. Additionally, to test our model we develop a stochastic rule-based traffic agent capable of generating interactive behaviors that can be used as a benchmark for simulating traffic participants in a crowded merge setting.

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Safe Reinforcement Learning on Autonomous Vehicles

Cited by 56 publications

References 25 publications

A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms

A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms

Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review

Interactive Decision Making for Autonomous Vehicles in Dense Traffic

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