Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827005
Carl Hynén Ulfsjöö, Daniel Axehill
Motion planning and decision-making while considering uncertainty is critical for an autonomous vehicle to safely and efficiently drive on a highway. This paper presents a new combined two-step approach for this problem, where a partially observable Markov decision process (POMDP) is tightly coupled with a scenario model predictive control (SCMPC) step. To generate the scenarios in the SCMPC step, the solution to the POMDP is used together with a novel scenario-reduction procedure, which selects a small representative subset of all scenarios considered in the POMDP. The resulting planner is evaluated in a simulation study where the impact of the two-step approach and the scenario-reduction method is shown.
{"title":"On Integrating POMDP and Scenario MPC for Planning under Uncertainty – with Applications to Highway Driving","authors":"Carl Hynén Ulfsjöö, Daniel Axehill","doi":"10.1109/iv51971.2022.9827005","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827005","url":null,"abstract":"Motion planning and decision-making while considering uncertainty is critical for an autonomous vehicle to safely and efficiently drive on a highway. This paper presents a new combined two-step approach for this problem, where a partially observable Markov decision process (POMDP) is tightly coupled with a scenario model predictive control (SCMPC) step. To generate the scenarios in the SCMPC step, the solution to the POMDP is used together with a novel scenario-reduction procedure, which selects a small representative subset of all scenarios considered in the POMDP. The resulting planner is evaluated in a simulation study where the impact of the two-step approach and the scenario-reduction method is shown.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123053823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/IV51971.2022.9827122
H. Schöner
This paper discusses main factors which establish responsible driving and the consequences for technical provisions, which are needed to support evidence of responsible behavior in autonomous driving. It relates these arguments to the concept of Tactical Safety: act early and proactively in traffic situations, in order to avoid non-controllable situations with possibly high accident severity. A continuous safety score, which serves to measure danger as the distance to a collision, is an essential utility for vigilant monitoring and gentle intervention previous to criticality. As a second prerequisite, a dependable communication community for traffic, road and environmental conditions enables early recognition of conditions for possible dangers beyond the accessible range of onboard sensors. Finally, the combination of those aspects for the safety assurance of autonomous vehicles is discussed.
{"title":"Assuring Responsible Driving of Autonomous Vehicles","authors":"H. Schöner","doi":"10.1109/IV51971.2022.9827122","DOIUrl":"https://doi.org/10.1109/IV51971.2022.9827122","url":null,"abstract":"This paper discusses main factors which establish responsible driving and the consequences for technical provisions, which are needed to support evidence of responsible behavior in autonomous driving. It relates these arguments to the concept of Tactical Safety: act early and proactively in traffic situations, in order to avoid non-controllable situations with possibly high accident severity. A continuous safety score, which serves to measure danger as the distance to a collision, is an essential utility for vigilant monitoring and gentle intervention previous to criticality. As a second prerequisite, a dependable communication community for traffic, road and environmental conditions enables early recognition of conditions for possible dangers beyond the accessible range of onboard sensors. Finally, the combination of those aspects for the safety assurance of autonomous vehicles is discussed.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115766744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827125
Weize Zhang, P. Yadmellat, Zhiwei Gao
Motion Planning is one of the key modules in autonomous driving systems to generate trajectories for self-driving vehicles. Spatio-temporal motion planners are often used to tackle complicated and dynamic driving scenarios. While effective in dealing with temporal changes in the environment, the existing methods are limited to optimizing a particular family of cost functions defined based on decoupled longitudinal and lateral terms. However, the planning objectives can only be explained using coupled terms in some cases, e.g. closeness to the reference path, lateral acceleration, and heading rate. The limitation arises from expressing such objectives as linear and quadratic terms suitable for optimization. This paper proposes an approach with theoretical proofs to approximate the upper bound of a given couple, nonlinear cost term with a set of uncoupled terms, allowing for converting the planning optimization problem into a linear quadratic optimization. The effectiveness of the proposed approach is shown through a series of simulated scenarios. The proposed approach results in smoother and steadier trajectories in the spatial plane.
{"title":"Spatial Optimization in Spatio-temporal Motion Planning","authors":"Weize Zhang, P. Yadmellat, Zhiwei Gao","doi":"10.1109/iv51971.2022.9827125","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827125","url":null,"abstract":"Motion Planning is one of the key modules in autonomous driving systems to generate trajectories for self-driving vehicles. Spatio-temporal motion planners are often used to tackle complicated and dynamic driving scenarios. While effective in dealing with temporal changes in the environment, the existing methods are limited to optimizing a particular family of cost functions defined based on decoupled longitudinal and lateral terms. However, the planning objectives can only be explained using coupled terms in some cases, e.g. closeness to the reference path, lateral acceleration, and heading rate. The limitation arises from expressing such objectives as linear and quadratic terms suitable for optimization. This paper proposes an approach with theoretical proofs to approximate the upper bound of a given couple, nonlinear cost term with a set of uncoupled terms, allowing for converting the planning optimization problem into a linear quadratic optimization. The effectiveness of the proposed approach is shown through a series of simulated scenarios. The proposed approach results in smoother and steadier trajectories in the spatial plane.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127086755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827215
Mattis Hoppe, J. C. Kirchhof, Evgeny Kusmenko, Changho Lee, Bernhard Rumpe
Agent-based simulation is an important testing tool for the development of autonomous vehicle software. Simulators enable engineers to test autonomous driving behavior in virtual environments, which is cheaper, faster, and safer than using a physical vehicle. An important aspect of autonomous driving software is its real-time capability, i.e. its ability to react to unforeseen events and new sensor inputs within a very short amount of time to prevent accidents. In this paper, we present a modular agent-based simulator architecture, which not only simulates the physical behavior of the vehicle, controlled by the software under test, but also its electrical/electronic (E/E) network. In particular, each ECU is simulated using a hardware emulator, which enables us to test the software as if it is run on the actual target hardware. Furthermore, the hardware emulator estimates the execution delays for the software under test, which enables more realistic approximations of the real behavior. In an evaluation example we analyze empirically how well the timing estimates reflect the reality. We show that modeling the memory hierarchy and instruction decoding has a crucial effect on the precision of this estimation.
{"title":"Agent-Based Autonomous Vehicle Simulation with Hardware Emulation in the Loop","authors":"Mattis Hoppe, J. C. Kirchhof, Evgeny Kusmenko, Changho Lee, Bernhard Rumpe","doi":"10.1109/iv51971.2022.9827215","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827215","url":null,"abstract":"Agent-based simulation is an important testing tool for the development of autonomous vehicle software. Simulators enable engineers to test autonomous driving behavior in virtual environments, which is cheaper, faster, and safer than using a physical vehicle. An important aspect of autonomous driving software is its real-time capability, i.e. its ability to react to unforeseen events and new sensor inputs within a very short amount of time to prevent accidents. In this paper, we present a modular agent-based simulator architecture, which not only simulates the physical behavior of the vehicle, controlled by the software under test, but also its electrical/electronic (E/E) network. In particular, each ECU is simulated using a hardware emulator, which enables us to test the software as if it is run on the actual target hardware. Furthermore, the hardware emulator estimates the execution delays for the software under test, which enables more realistic approximations of the real behavior. In an evaluation example we analyze empirically how well the timing estimates reflect the reality. We show that modeling the memory hierarchy and instruction decoding has a crucial effect on the precision of this estimation.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127460880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827397
Kunming Li, Mao Shan, Stuart Eiffert, Stewart Worrall, E. Nebot
Autonomous vehicle navigation in shared pedestrian environments requires the ability to predict future crowd motion as well as understand human behaviour. However, most existing methods predict pedestrian future motion without considering potential collisions within the crowd. Furthermore, most current predictive models are tested on datasets that assume full observability of the crowd by relying on a top-down view, which does not reflect the real-world use case of autonomous vehicles due to the inherent limitations of on-board sensors such as visual occlusion. Inspired by prior works, we propose a pedestrian motion prediction model trained via contrastive learning, improving prediction accuracy as well as forecasting collision-free trajectories. Additionally, we propose a method for implementing a predictor using a multi-pedestrian probabilistic tracker, which fuses multiple on-board sensors to track pedestrians in 3D space. Through comprehensive experiments on both aerial view and driving datasets collected in a real-world urban environment, we show that our proposed method improves on state of art methods with better prediction accuracy and more socially acceptable prediction trajectories.
{"title":"Towards Collision-Free Probabilistic Pedestrian Motion Prediction for Autonomous Vehicles","authors":"Kunming Li, Mao Shan, Stuart Eiffert, Stewart Worrall, E. Nebot","doi":"10.1109/iv51971.2022.9827397","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827397","url":null,"abstract":"Autonomous vehicle navigation in shared pedestrian environments requires the ability to predict future crowd motion as well as understand human behaviour. However, most existing methods predict pedestrian future motion without considering potential collisions within the crowd. Furthermore, most current predictive models are tested on datasets that assume full observability of the crowd by relying on a top-down view, which does not reflect the real-world use case of autonomous vehicles due to the inherent limitations of on-board sensors such as visual occlusion. Inspired by prior works, we propose a pedestrian motion prediction model trained via contrastive learning, improving prediction accuracy as well as forecasting collision-free trajectories. Additionally, we propose a method for implementing a predictor using a multi-pedestrian probabilistic tracker, which fuses multiple on-board sensors to track pedestrians in 3D space. Through comprehensive experiments on both aerial view and driving datasets collected in a real-world urban environment, we show that our proposed method improves on state of art methods with better prediction accuracy and more socially acceptable prediction trajectories.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125854133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827311
Jin Yan, Jérôme Härri
Leveraging Modulation and Coding Schemes (MCS) in 5G New Radio (NR) Sidelink represents one key strategy to provide sufficient capacity required by future 5G for Vehicle-to-Everything (V2X) services for intelligent vehicles. Early studies either directly adopt the previously optimised QPSK 1/2 by 802.11p/C-V2X or suggest an optimal MCS value under a particular context. In this paper, we identify a MCS value optimal under any context, by evaluating the impact of MCS on V2X broadcast communication considering multiple varying parameters (e.g. variable packet size, transmit rate or density) representative of different 5G V2X services.
{"title":"MCS Analysis for 5G-NR V2X Sidelink Broadcast Communication","authors":"Jin Yan, Jérôme Härri","doi":"10.1109/iv51971.2022.9827311","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827311","url":null,"abstract":"Leveraging Modulation and Coding Schemes (MCS) in 5G New Radio (NR) Sidelink represents one key strategy to provide sufficient capacity required by future 5G for Vehicle-to-Everything (V2X) services for intelligent vehicles. Early studies either directly adopt the previously optimised QPSK 1/2 by 802.11p/C-V2X or suggest an optimal MCS value under a particular context. In this paper, we identify a MCS value optimal under any context, by evaluating the impact of MCS on V2X broadcast communication considering multiple varying parameters (e.g. variable packet size, transmit rate or density) representative of different 5G V2X services.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123292020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827224
Philipp Wintersberger, Shadan Sadeghian Borojeni, Clemens Schartmüller, Anna-Katharina Frison, A. Riener
Studies on imminent Take-Over Requests (TORs) in automated driving have mainly addressed safety aspects rather than user experience (UX). In this study, we investigated the fulfillment of user needs during SAE L3 driving on a test track. Participants engaged in non-driving related tasks (NDRTs; using a smartphone or the auditory modality) had to respond to critical TORs to prevent an accident. Our results, based on qualitative methods, show that participants expect L3 vehicles to be safe and confirmed this assessment after the test track experience. Furthermore, participants preferred NDRTs using the auditory modality over the smartphone to maintain situation awareness. Our study indicates that drivers may behave responsibly in L3 vehicles, provided they are supported with user interfaces that fulfill their psychological needs.
{"title":"User Experience Evaluation of SAE Level 3 Driving on a Test Track","authors":"Philipp Wintersberger, Shadan Sadeghian Borojeni, Clemens Schartmüller, Anna-Katharina Frison, A. Riener","doi":"10.1109/iv51971.2022.9827224","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827224","url":null,"abstract":"Studies on imminent Take-Over Requests (TORs) in automated driving have mainly addressed safety aspects rather than user experience (UX). In this study, we investigated the fulfillment of user needs during SAE L3 driving on a test track. Participants engaged in non-driving related tasks (NDRTs; using a smartphone or the auditory modality) had to respond to critical TORs to prevent an accident. Our results, based on qualitative methods, show that participants expect L3 vehicles to be safe and confirmed this assessment after the test track experience. Furthermore, participants preferred NDRTs using the auditory modality over the smartphone to maintain situation awareness. Our study indicates that drivers may behave responsibly in L3 vehicles, provided they are supported with user interfaces that fulfill their psychological needs.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115060639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827411
Fumiaki Sato, T. Koshizen
To reduce sideswipes and collision accidents involving two- and four-wheeled vehicles under mixed traffic flow conditions, we previously created a smartphone application (app) that predicts acceleration and driving lane behaviors of two-wheeled vehicles. In this system, vehicles are detected from road images taken by a smartphone camera, and vehicles positions on the road are estimated by our projection conversion algorithm. However, regarding that app, it is necessary to improve the accuracy of the vanishing point calculations in the camera images. Accordingly, in order to reduce calculation costs, we created a method that integrates road segmentation and vehicle detection to create a new scheme for detecting road edges and the vanishing point, even on roads without lane lines. These improvements will help maintain the accuracy of vanishing point calculations while facilitating their high real-time characteristics.
{"title":"Improved Vanishing Point Accuracy by Integrating Vehicle Detection and Segmentation","authors":"Fumiaki Sato, T. Koshizen","doi":"10.1109/iv51971.2022.9827411","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827411","url":null,"abstract":"To reduce sideswipes and collision accidents involving two- and four-wheeled vehicles under mixed traffic flow conditions, we previously created a smartphone application (app) that predicts acceleration and driving lane behaviors of two-wheeled vehicles. In this system, vehicles are detected from road images taken by a smartphone camera, and vehicles positions on the road are estimated by our projection conversion algorithm. However, regarding that app, it is necessary to improve the accuracy of the vanishing point calculations in the camera images. Accordingly, in order to reduce calculation costs, we created a method that integrates road segmentation and vehicle detection to create a new scheme for detecting road edges and the vanishing point, even on roads without lane lines. These improvements will help maintain the accuracy of vanishing point calculations while facilitating their high real-time characteristics.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116410810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827144
P. Colling, Dennis Müller, M. Rottmann
We present a procedure to create high definition maps of lanes based on detected and tracked vehicles from perception sensor data as well as the ego vehicle using multiple observations of the same location. The procedure consists of two parts. First, an aggregation part in which the detected and tracked road users as well as the driving path of the ego vehicle are aggregated into a map representation. Second, lanes are extracted from those maps as lane center lines in a structured data format. The final lane centers are represented in a directed graph representation including the driving direction. They are accurate up to a few centimeters. Our procedure is not restricted to any environment and does not rely on any prior map information. In our experiments with real world data and available ground truth, we study the performance of different map aggregations e.g., based on the ego vehicle only or based on other road users. Furthermore, we study the dependence on the number of data recording repetitions.
{"title":"HD Lane Map Generation Based on Trail Map Aggregation","authors":"P. Colling, Dennis Müller, M. Rottmann","doi":"10.1109/iv51971.2022.9827144","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827144","url":null,"abstract":"We present a procedure to create high definition maps of lanes based on detected and tracked vehicles from perception sensor data as well as the ego vehicle using multiple observations of the same location. The procedure consists of two parts. First, an aggregation part in which the detected and tracked road users as well as the driving path of the ego vehicle are aggregated into a map representation. Second, lanes are extracted from those maps as lane center lines in a structured data format. The final lane centers are represented in a directed graph representation including the driving direction. They are accurate up to a few centimeters. Our procedure is not restricted to any environment and does not rely on any prior map information. In our experiments with real world data and available ground truth, we study the performance of different map aggregations e.g., based on the ego vehicle only or based on other road users. Furthermore, we study the dependence on the number of data recording repetitions.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122291208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827393
Liangxu Sun, Meng-Zhuo Liu, Huayi Zhan, Yingie Wu
Many methods in mobile robotics usually utilize current sensor measurement to evaluate the localization performance of robots, for example in scan matching and particle filter methods. This immediately detecting methodology tend to cause a problem that a well-localization robot obtains a poor sensor measurement, the robot may mistake momentary observation noise for a localization failure. In this paper, we propose a new robot localization fault detection method for resolving this problem. We model robot localization fault detection as a sequential decision-making problem, where the decision of detecting a localization failure is based on a long-term sensor measurements. We employ two parameters of false-positive and false-negative observation error probabilities, which can eliminate the influence of noisy observations. Further, the proposed method derives Bayesian update equations for the integration of a long-term observations and presents an analytic formula representing the belief function of the reliability of localization results. Experimental studies validate the effectiveness of the proposed method.
{"title":"A Sequential Decision-theoretic Method for Detecting Mobile Robots Localization Failures","authors":"Liangxu Sun, Meng-Zhuo Liu, Huayi Zhan, Yingie Wu","doi":"10.1109/iv51971.2022.9827393","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827393","url":null,"abstract":"Many methods in mobile robotics usually utilize current sensor measurement to evaluate the localization performance of robots, for example in scan matching and particle filter methods. This immediately detecting methodology tend to cause a problem that a well-localization robot obtains a poor sensor measurement, the robot may mistake momentary observation noise for a localization failure. In this paper, we propose a new robot localization fault detection method for resolving this problem. We model robot localization fault detection as a sequential decision-making problem, where the decision of detecting a localization failure is based on a long-term sensor measurements. We employ two parameters of false-positive and false-negative observation error probabilities, which can eliminate the influence of noisy observations. Further, the proposed method derives Bayesian update equations for the integration of a long-term observations and presents an analytic formula representing the belief function of the reliability of localization results. Experimental studies validate the effectiveness of the proposed method.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114182022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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