Extracting Human-Like Driving Behaviors From Expert Driver Data Using Deep Learning

Sama, Kyle; Morales, Yoichi; Liu, Hailong; Akai, Naoki; Carballo, Alexander; Takeuchi, Eijiro; Takeda, Kazuya

doi:10.1109/tvt.2020.2980197

Cited by 50 publications

(19 citation statements)

References 34 publications

(43 reference statements)

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“…The weights of such features were then obtained through Inverse Reinforcement Learning (IRL). Reliance on hand-crafted features in [20] was replaced by the use of deep auto-encoders in [21]. While learning-based methods have proven to be very effective at replicating the driving behaviors of experts, the inexplicability of their output is a major drawback of using these approaches.…”

Section: Learning-based Approachesmentioning

confidence: 99%

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

et al. 2021

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A common approach used for planning blind intersection crossings is to assume that hypothetical vehicles are approaching the intersection at a constant speed from the occluded areas. Such an assumption can result in a deadlock problem, causing the ego vehicle to remain stopped at an intersection indefinitely due to insufficient visibility. To solve this problem and facilitate safe, deadlock-free intersection crossing, we propose a blind intersection planner that utilizes both the ego vehicle and the approaching vehicle’s visibility. The planner uses a particle filter and our proposed visibility-dependent behavior model of approaching vehicles for predicting hidden vehicles. The behavior model is designed based on an analysis of actual driving data from multiple drivers crossing blind intersections. The proposed planner was tested in a simulation and found to be effective for allowing deadlock-free crossings at intersections where a baseline planner became stuck in a deadlock. The effects of perception accuracy and sensor position on output motion were also investigated. It was found that the proposed planner delayed crossing motion when the perception was imperfect. Furthermore, our results showed that the planner decelerated less while crossing the intersection with the front-mounted sensor configuration compared to the roof-mounted configuration due to the improved visibility. The minimum speed difference between the two sensor configurations was 1.82 m/s at an intersection with relatively poor visibility and 1.50 m/s at an intersection with good visibility.

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Section: Learning-based Approachesmentioning

confidence: 99%

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

et al. 2021

Self Cite

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“…Among them, learning-based approaches are promising and gaining popularity (Kiran et al, 2021). Some studies that human driving behaviors can be extracted through the machine learning algorithms, such as deep learning (Sama et al, 2020;Huang et al, 2020), imitation learning (Rehder et al, 2017), and inverse reinforcement learning (Sadigh et al, 2016). However, due to the inherent black-box nature of the neural networks, the interpret ability of learning based methods is not ideal.…”

Section: The Decision-making Behind the Scenesmentioning

confidence: 99%

“…Besides, as a human has limited perception capability, the information one can obtain from the surrounding environment is limited (Dingus et al, 2016;Li and Busso, 2016;Kuo et al, 2019;. Further, their individual behaviors are usually highly personalized, as different road users have diverse travel demands, preferences and habits (Fridman et al, 2019;Martinez et al, 2018;Sama et al, 2020; . Thus, for the scene generation for autonomous driving, it is worthwhile exploring intelligent methods which can realize naturalistic and human-like interactive behaviors between intelligent agents.…”

Section: Introductionmentioning

confidence: 99%

Human-like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game-theoretic Approach with Turing Test

Zhang¹,

Hang²,

Huang³

et al. 2021

Preprint

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Interacting with surrounding road users is a key feature of vehicles and is critical for intelligence testing of autonomous vehicles. The Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart and can hardly reflect human-like behaviors in real world driving scenarios. To further improve the technology, in this work we present a novel hierarchical game-theoretical framework to represent naturalistic multi-modal interactions among road users in simulation and testing, which is then validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is utilized to model the decision-making process. Then, a probing behavior is generated by the proposed game theoretic model, and is further applied to control the vehicle via Markov chain. To validate the feasibility and effectiveness, the proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human-likeness of the proposed algorithm. The experiment results show that the proposed Bayesian game theoretic framework can effectively generate representative scenes of human-like decision-making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. Corresponding author(s) Email: lyuchen@ntu.edu.sg

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“…[ 18 ] Among them, learning‐based approaches are promising and gaining popularity. [ 17 ] Some studies have realized human driving behaviors generation using machine learning algorithms, such as deep learning, [ 12,19 ] imitation learning, [ 20 ] and inverse reinforcement learning. [ 21 ] However, owing to the inherent black‐box nature of the neural networks, the interpreting ability of learning based methods is not ideal.…”

Section: Introductionmentioning

confidence: 99%

“…[6][7][8][9] Further, individual behaviors are highly personalized, as different road users have diverse travel demands, preferences, and habits. [10][11][12][13] Thus, it is worthwhile exploring intelligent methods which can realize naturalistic and human-like interactive behaviors between intelligent agents for scene generation. Rather than establishing comprehensive and various large-scale scenes, we focus on the intelligent representation of interacting moments.…”

mentioning

confidence: 99%

Human‐Like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game‐Theoretic Approach with Turing Test

Zhang

Hang

Huang

et al. 2022

Advanced Intelligent Systems

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Interacting with surrounding road users is a key feature of autonomous vehicles and is critical for their intelligence testing. Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart, and they barely reflect human‐like behaviors in real‐world driving scenarios. Therefore, a novel hierarchical game‐theoretical framework is presented to represent naturalistic multimodal interactions among road users during simulation and testing, which is subsequently validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is used to model the decision‐making process. Next, a probing behavior is generated by the proposed game‐theoretic model, and is further applied to control the vehicle via the Markov chain. The proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human‐likeness of the proposed algorithm. The experimental results show that the proposed Bayesian game‐theoretic framework can effectively generate representative scenes of human‐like decision‐making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. An interactive preprint version of the article can be found at: https://www.authorea.com/doi/full/10.22541/au.163482630.00283278.

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Extracting Human-Like Driving Behaviors From Expert Driver Data Using Deep Learning

Cited by 50 publications

References 34 publications

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

Human-like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game-theoretic Approach with Turing Test

Human‐Like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game‐Theoretic Approach with Turing Test

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