An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

Shin, Donghoon; Yi, Subin; Park, Kang-moon; Park, Manbok

doi:10.3390/app10062138

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…When F is linear, we can use the Kalman filter to estimate x t from the noisy measurement. Given the estimated state xt−1 at time step t − 1, the prior estimate of x t is 5 xt|t−1 = Fx t−1 .…”

Section: Appendix a Formulation Of Kalman Filtermentioning

confidence: 99%

Communication Scheduling by Deep Reinforcement Learning for Remote Traffic State Estimation With Bayesian Inference

Peng

Xie

Seco‐Granados³

et al. 2022

IEEE Trans. Veh. Technol.

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Traffic awareness is the prerequisite of autonomous driving. Given the limitation of on-board sensors (e.g., precision and price), remote measurement from either infrastructure or other vehicles can improve traffic safety. However, the wireless communication carrying the measurement result undergoes fading, noise and interference and has a certain probability of outage. When the communication fails, the vehicle state can only be predicted by Bayesian filtering with a low precision. Higher communication resource utilization (e.g., transmission power) reduces the outage probability and hence results in an improved estimation precision. The power control subject to an estimate variance constraint is a difficult problem due to the complicated mapping from transmit power to vehicle-state estimate variance. In this paper, we develop an estimator consisting of several Kalman filters (KFs) or extended Kalman filters (EKFs) and an interacting multiple model (IMM) to estimate and predict the vehicle state. We propose to apply deep reinforcement learning (DRL) for the transmit power optimization. In particular, we consider an intersection and a lane-changing scenario and apply proximal policy optimization (PPO) and soft actor-critic (SAC) to train the DRL model. Testing results show satisfactory power control strategies confining estimate variances below given threshold. SAC achieves higher performance compared to PPO.

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Section: Appendix a Formulation Of Kalman Filtermentioning

confidence: 99%

Communication Scheduling by Deep Reinforcement Learning for Remote Traffic State Estimation With Bayesian Inference

Peng

Xie

Seco‐Granados³

et al. 2022

IEEE Trans. Veh. Technol.

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“…There are two control levels separated within the control processes of the SciL framework. The first control level handles the decision making processes of the individual scenario components, like the vehicle/vehicles under test, the pedestrians, the cyclists or the simulated or real traffic management system [98]- [100]. The higher-level control manages the overall coordination of the traffic scenario, taking into account the critical states of the systems, the corner cases, and the implemented test scenario [101].…”

Section: ) Scil Controlmentioning

confidence: 99%

Next Generation X-in-the-Loop Validation Methodology for Automated Vehicle Systems

Szalay

2021

IEEE Access

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Testing self-driving vehicles is still a new and immature process; the globally harmonised procedure expected much later. The resource-demanding nature of real-world tests makes it indispensable to develop and improve the efficiency of virtual environment based testing methods. Accordingly, a novel Xin-the-Loop framework is proposed to fully exploit the recent advances in info-communication technologies, vehicle automation, and testing and validation requirements. This methodology real-time connects physical and virtual testing with high correlation while completely blurs the sharp boundaries between them. Measurement results confirm the superior performance of the 5G communication link in providing a stable, real-time connection between the real world and its virtual representation. The live demonstration proved the presented concept at the newly constructed Hungarian proving ground for automated driving. The performed investigation also includes comprehensive benchmarking, focusing on the most up-to-date automotive testing frameworks. The analysis considers the methodologies and techniques applied by the most relevant actors in the automotive testing sector worldwide. Accordingly, the newly developed testing framework is evaluated and validated in light of the state-of-the-art methods used by the automotive industry.INDEX TERMS Automotive proving ground, automated vehicle control, autonomous vehicle, co-simulation, digital twin, mixed-reality testing, scenario-based testing, testing and validation, virtual and mixed reality, V-model, X-in-the-loop testing.

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“…Recently, vehicles with partially automated driving capabilities developed from advanced driver assistance systems (ADASs) have been competitively introduced by major carmakers [1,2]. Most vehicle manufacturers aim to commercialize fully automated driving cars in the near future, and they have concentrated on autonomous driving research to satisfy both safety and marketability.…”

Section: Introductionmentioning

confidence: 99%

High Definition Map-Based Localization Using ADAS Environment Sensors for Application to Automated Driving Vehicles

Shin

Park

2020

Applied Sciences

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This paper presents high definition (HD) map-based localization using advanced driver assistance system (ADAS) environment sensors for application to automated driving vehicles. A variety of autonomous driving technologies are being developed using expensive and high-performance sensors, but limitations exist due to several practical issues. In respect of the application of autonomous driving cars in the near future, it is necessary to ensure autonomous driving performance by effectively utilizing sensors that are already installed for ADAS purposes. Additionally, the most common localization algorithm, which is usually used lane information only, has a highly unstable disadvantage in the absence of that information. Therefore, it is essential to ensure localization performance with other road features such as guardrails when there are no lane markings. In this study, we would like to propose a localization algorithm that could be implemented in the near future by using low-cost sensors and HD maps. The proposed localization algorithm consists of several sections: environment feature representation with low-cost sensors, digital map analysis and application, position correction based on map-matching, designated validation gates, and extended Kalman filter (EKF)-based localization filtering and fusion. Lane information is detected by monocular vision in front of the vehicle. A guardrail is perceived by radar by distinguishing low-speed object measurements and by accumulating several steps to extract wall features. These lane and guardrail information are able to correct the host vehicle position by using the iterative closest point (ICP) algorithm. The rigid transformation between the digital high definition map (HD map) and environment features is calculated through ICP matching. Each corrected vehicle position by map-matching is selected and merged based on EKF with double updating. The proposed algorithm was verified through simulation based on actual driving log data.

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An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

Cited by 9 publications

References 20 publications

Communication Scheduling by Deep Reinforcement Learning for Remote Traffic State Estimation With Bayesian Inference

Communication Scheduling by Deep Reinforcement Learning for Remote Traffic State Estimation With Bayesian Inference

Next Generation X-in-the-Loop Validation Methodology for Automated Vehicle Systems

High Definition Map-Based Localization Using ADAS Environment Sensors for Application to Automated Driving Vehicles

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