Detection of Localization Failures Using Markov Random Fields With Fully Connected Latent Variables for Safe LiDAR-Based Automated Driving

Akai, Naoki; Akagi, Yasuhiro; Hirayama, Takatsugu; Morikawa, Takayuki; Murase, Hiroshi

doi:10.1109/tits.2022.3164397

Cited by 8 publications

(3 citation statements)

References 52 publications

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“…. These estimated values are well-matched with those reported in previous research [34], where maximum errors in heading and lateral position were found to be 1.05 deg and 0.13 m, respectively.…”

Section: Preliminary Investigation On Characteristics Of Localization...supporting

confidence: 90%

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Jo,

Kwon,

2024

IEEE Access

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This study proposes a novel motion planning strategy to address localization uncertainty in autonomous buses. Conventional motion planning algorithms utilize information from high-definition (HD) maps to overcome the limited detection range of on-board sensors. However, this information contains uncertainty due to the utilization of estimated localization results during reference frame transformation. The wider dimensions of autonomous buses, compared to regular vehicles, amplify the potential dangers associated with localization uncertainty. Therefore, this research focuses on enhancing motion planning for autonomous buses by effectively addressing localization uncertainty. The investigation of manual driving data from autonomous buses highlights the need to handle three issues: heading bias, lateral position error, and longitudinal position error. Firstly, the heading bias was dealt with by implementing an Offset-free Model Predictive Control (OF-MPC) with a Moving Horizon Estimation (MHE) scheme for lateral motion planning. Secondly, the lateral position error was handled by incorporating a drivable corridor to determine the desired path. Lastly, the longitudinal position error was resolved by implementing a chance-constrained MPC for longitudinal motion planning. The proposed approach showed noticeable enhancement in path tracking performance while still securing ride comfort in lateral motion, collision safety, and prevention of stop-line violations. We evaluated the feasibility of the proposed approach through vehicle tests on a test track, and its applicability was further confirmed through fully autonomous driving tests on actual urban bus-only lanes.INDEX TERMS Autonomous bus, autonomous driving, chance-constrained model predictive control, localization uncertainty, moving horizon estimation, offset-free model predictive control.

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Section: Preliminary Investigation On Characteristics Of Localization...supporting

confidence: 90%

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Jo,

Kwon,

2024

IEEE Access

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“…Alternatively, researchers utilised Markov random fields with fully-connected latent variables, highlighting that the connections enable their model to consider the entire relation, and aim to identify misalignment, and localisation errors due to misalignment in [110]. They later extended and tested their mechanism for 3D LIDAR-based localisation in automated driving systems in [111]. Identifying obstacles and moving objects without focusing on their classes is another functionality provided by perception subsystem.…”

Section: Other Tasksmentioning

confidence: 99%

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Yatbaz,

Dianati,

Woodman

2024

IEEE Trans. Intell. Transport. Syst.

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“…Ideally, global localization has to be performed only when localization has failed. However, exact failure detection of localization is challenging owing to the use of the independent assumption to the LiDAR measurements (Akai et al, 2022(Akai et al, , 2019Thrun et al, 2005). Even though the presented method estimates reliability of the localization result, it is difficult to perfectly classify whether localization has failed using the estimated reliability.…”

Section: Global Localization and Its Fusion With Pose Trackingmentioning

confidence: 99%

Reliable Monte Carlo localization for mobile robots

Akai

2023

Journal of Field Robotics

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Reliability is a key factor for realizing safety guarantee of fully autonomous robot systems.In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick relocalization, simultaneously. The presented method can be implemented using a similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables a robot to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick relocalization from a failure state can be realized while mitigating noisy influence of global localization. We conduct three types of experiments using wheeled mobile robots equipped with a two-dimensional LiDAR. Results show that reliable MCL that performs robust localization, self-failure detection, and quick failure recovery can be realized.

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Detection of Localization Failures Using Markov Random Fields With Fully Connected Latent Variables for Safe LiDAR-Based Automated Driving

Cited by 8 publications

References 52 publications

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Reliable Monte Carlo localization for mobile robots

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