Pub Date : 2018-11-01DOI: 10.1109/ITSC.2018.8569492
Jie Sun, Jian Sun
This paper studies the traffic oscillations under connected environment by connecting it with vehicles' car-following behaviors. The manual vehicles are calibrated with the intelligent driver model to reproduce the traffic oscillations by simulating heterogeneous vehicles driving on a single lane. The characteristics of traffic oscillations are then investigated with the second-order difference of cumulative data method. With the incorporation of different ratios of connected and automated vehicles (CAVs), we study its impact on traffic flow and propose several control strategies (responsive control without connection, proactive control with V2V connection, and traffic wave suppression control with V2I connection) with low-penetration CAVs. The performance of these control strategies are then compared, while the proposed control strategies can improve traffic flow operation by decreasing traffic oscillation occurrences.
{"title":"Investigating the oscillation characteristics and mitigating its impact with low-penetration connected and automated vehicles","authors":"Jie Sun, Jian Sun","doi":"10.1109/ITSC.2018.8569492","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569492","url":null,"abstract":"This paper studies the traffic oscillations under connected environment by connecting it with vehicles' car-following behaviors. The manual vehicles are calibrated with the intelligent driver model to reproduce the traffic oscillations by simulating heterogeneous vehicles driving on a single lane. The characteristics of traffic oscillations are then investigated with the second-order difference of cumulative data method. With the incorporation of different ratios of connected and automated vehicles (CAVs), we study its impact on traffic flow and propose several control strategies (responsive control without connection, proactive control with V2V connection, and traffic wave suppression control with V2I connection) with low-penetration CAVs. The performance of these control strategies are then compared, while the proposed control strategies can improve traffic flow operation by decreasing traffic oscillation occurrences.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132441616","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 : 2018-11-01DOI: 10.1109/ITSC.2018.8569753
Jiaqi Ma, Fang Zhou, Zhitong Huang, Rachel James
Most existing studies on connected and automated vehicle (CAV) applications apply simulation to evaluate system effectiveness. Model accuracy, limited data for calibration, and simulation assumptions limit the validity of evaluation results. One alternative approach is to use emerging hardware-in-the-loop (HIL) testing methods that allow physical test vehicles to interact with virtual vehicles from traffic simulation models. This provides an evaluation environment that can replicate deployment conditions at early stages of CAV implementation-without incurring excessive costs related to large field tests. In this study, a hardware-in-the-loop (HIL) testing system for CAV applications is proposed. Cooperative adaptive cruise control (CACC) is one of the key CAV applications that has received a lot of attention. This study develops a HIL proof-of-concept testing prototype for CAV applications in a vehicle-to-vehicle (V2V) environment. The contributions of this study include developing a HIL testing architecture for V2V-based CAV applications; implementing the proposed HIL architecture; developing a prototype; and testing CACC as the selected use case to observe HIL system performance. The results of this effort also contribute to a better understanding of CACC string performance.
{"title":"Hardware-In-The-Loop Testing of Connected and Automated Vehicle Applications: A Use Case For Cooperative Adaptive Cruise Control","authors":"Jiaqi Ma, Fang Zhou, Zhitong Huang, Rachel James","doi":"10.1109/ITSC.2018.8569753","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569753","url":null,"abstract":"Most existing studies on connected and automated vehicle (CAV) applications apply simulation to evaluate system effectiveness. Model accuracy, limited data for calibration, and simulation assumptions limit the validity of evaluation results. One alternative approach is to use emerging hardware-in-the-loop (HIL) testing methods that allow physical test vehicles to interact with virtual vehicles from traffic simulation models. This provides an evaluation environment that can replicate deployment conditions at early stages of CAV implementation-without incurring excessive costs related to large field tests. In this study, a hardware-in-the-loop (HIL) testing system for CAV applications is proposed. Cooperative adaptive cruise control (CACC) is one of the key CAV applications that has received a lot of attention. This study develops a HIL proof-of-concept testing prototype for CAV applications in a vehicle-to-vehicle (V2V) environment. The contributions of this study include developing a HIL testing architecture for V2V-based CAV applications; implementing the proposed HIL architecture; developing a prototype; and testing CACC as the selected use case to observe HIL system performance. The results of this effort also contribute to a better understanding of CACC string performance.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134166685","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 : 2018-11-01DOI: 10.1109/ITSC.2018.8569726
Cheng Zhang, Linan Zhang, Yangdong Liu, Xiaoguang Yang
Bike-sharing has experienced rapid development worldwide in the last decade. Accurate usage prediction is necessary to support timely reposition and ensure availability. Existing prediction methods of bike-sharing usage are mostly based on its own history. They can capture temporal characteristics under normal state but cannot respond to sudden events. Bike-sharing is a part of public transport. The interrelation of bike-sharing and other public transport systems can be utilized to capture the impact of sudden events. This prediction approach considers both historical usage and real-time passengers of public transport. Then use neural networks to establish the connection among them. Long short-term memory (LSTM) is adopted because it shows outstanding performance to learn long-term temporal dependencies. Experiments show that the prediction through this approach is more accurate than baselines. The mean absolute error (MAE) reduces by 25.32% to 39.82%. With the input of real-time boarding passengers, the prediction better responds to sudden changes, and the MAE value further reduces by 21.81%. This approach also outperforms others in different prediction horizons. Moreover, the methodology is applicable to both traditional and station-free bike-sharing systems. Data for the prediction are available in many cities, and hence it is ready for practice.
{"title":"Short-term Prediction of Bike-sharing Usage Considering Public Transport: A LSTM Approach","authors":"Cheng Zhang, Linan Zhang, Yangdong Liu, Xiaoguang Yang","doi":"10.1109/ITSC.2018.8569726","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569726","url":null,"abstract":"Bike-sharing has experienced rapid development worldwide in the last decade. Accurate usage prediction is necessary to support timely reposition and ensure availability. Existing prediction methods of bike-sharing usage are mostly based on its own history. They can capture temporal characteristics under normal state but cannot respond to sudden events. Bike-sharing is a part of public transport. The interrelation of bike-sharing and other public transport systems can be utilized to capture the impact of sudden events. This prediction approach considers both historical usage and real-time passengers of public transport. Then use neural networks to establish the connection among them. Long short-term memory (LSTM) is adopted because it shows outstanding performance to learn long-term temporal dependencies. Experiments show that the prediction through this approach is more accurate than baselines. The mean absolute error (MAE) reduces by 25.32% to 39.82%. With the input of real-time boarding passengers, the prediction better responds to sudden changes, and the MAE value further reduces by 21.81%. This approach also outperforms others in different prediction horizons. Moreover, the methodology is applicable to both traditional and station-free bike-sharing systems. Data for the prediction are available in many cities, and hence it is ready for practice.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131497836","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 : 2018-11-01DOI: 10.1109/ITSC.2018.8569953
M. Vallati, L. Chrpa
The current growth in urbanisation is posing serious problems in urban areas worldwide. Traditional traffic control tools, such as SCOOT and SCATS, are widely exploited in major cities, but given the increasing traffic demands, they need to be complemented with additional mechanisms. Within this context, the emerging interest in autonomous vehicles (AVs) points to the direction of a paradigm shift in the way in which traffic is controlled and managed. This is due to the fact that AVs can exploit different types of communication, hence take better informed decisions. Despite the amount of work dedicated to engineering solutions for supporting and implementing the different types of vehicular communication, there is a lack of analysis focusing on the implications of exploiting one (or more) type of communication. In this work, focusing on urban areas, we provide a principled and detailed analysis of the impact of different kinds of communication on reasoning capabilities of vehicles and of urban traffic control (e.g. level of deliberation). The outcome of the performed analysis can then be fruitfully exploited by experts to better understand and support communication and reasoning, according to the needs of the controlled areas.
{"title":"A Principled Analysis of the Interrelation between Vehicular Communication and Reasoning Capabilities of Autonomous Vehicles","authors":"M. Vallati, L. Chrpa","doi":"10.1109/ITSC.2018.8569953","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569953","url":null,"abstract":"The current growth in urbanisation is posing serious problems in urban areas worldwide. Traditional traffic control tools, such as SCOOT and SCATS, are widely exploited in major cities, but given the increasing traffic demands, they need to be complemented with additional mechanisms. Within this context, the emerging interest in autonomous vehicles (AVs) points to the direction of a paradigm shift in the way in which traffic is controlled and managed. This is due to the fact that AVs can exploit different types of communication, hence take better informed decisions. Despite the amount of work dedicated to engineering solutions for supporting and implementing the different types of vehicular communication, there is a lack of analysis focusing on the implications of exploiting one (or more) type of communication. In this work, focusing on urban areas, we provide a principled and detailed analysis of the impact of different kinds of communication on reasoning capabilities of vehicles and of urban traffic control (e.g. level of deliberation). The outcome of the performed analysis can then be fruitfully exploited by experts to better understand and support communication and reasoning, according to the needs of the controlled areas.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134416464","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 : 2018-11-01DOI: 10.1109/ITSC.2018.8569698
Ammar Haydari, Y. Yilmaz
Vehicular network (VANET), a special type of ad-hoc network, provides communication infrastructure for vehicles and related parties, such as road side units (RSU). Secure communication concerns are becoming more prevalent with the increasing technology usage in transportation systems. One of the major objectives in VANET is maintaining the availability of the system. Distributed Denial of Service (DDoS) attack is one of the most popular attack types aiming at the availability of system. We consider the timely detection and mitigation of DDoS attacks to RSU in Intelligent Transportation Systems (ITS). A novel framework for detecting and mitigating low-rate DDoS attacks in ITS based on nonparametric statistical anomaly detection is proposed. Dealing with low-rate DDoS attacks is challenging since they can bypass traditional data filtering techniques while threatening the RSU availability due to their highly distributed nature. Extensive simulation results are presented for a real road scenario with the help of the SUMO traffic simulation software. The results show that our proposed method significantly outperforms two parametric methods for timely detection based on the Cumulative Sum (CUSUM) test, as well as the traditional data filtering approach in terms of average detection delay and false alarm rate.
{"title":"Real-Time Detection and Mitigation of DDoS Attacks in Intelligent Transportation Systems","authors":"Ammar Haydari, Y. Yilmaz","doi":"10.1109/ITSC.2018.8569698","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569698","url":null,"abstract":"Vehicular network (VANET), a special type of ad-hoc network, provides communication infrastructure for vehicles and related parties, such as road side units (RSU). Secure communication concerns are becoming more prevalent with the increasing technology usage in transportation systems. One of the major objectives in VANET is maintaining the availability of the system. Distributed Denial of Service (DDoS) attack is one of the most popular attack types aiming at the availability of system. We consider the timely detection and mitigation of DDoS attacks to RSU in Intelligent Transportation Systems (ITS). A novel framework for detecting and mitigating low-rate DDoS attacks in ITS based on nonparametric statistical anomaly detection is proposed. Dealing with low-rate DDoS attacks is challenging since they can bypass traditional data filtering techniques while threatening the RSU availability due to their highly distributed nature. Extensive simulation results are presented for a real road scenario with the help of the SUMO traffic simulation software. The results show that our proposed method significantly outperforms two parametric methods for timely detection based on the Cumulative Sum (CUSUM) test, as well as the traditional data filtering approach in terms of average detection delay and false alarm rate.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134171981","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 : 2018-11-01DOI: 10.1109/ITSC.2018.8569738
Romain Saussard, S. Zair, G. Gil
Ego-motion estimation plays a crucial role in the achievement of autonomous driving and advanced driver assistance systems (ADAS). Classical approaches using wheel-based odometers suffers from errors due to the variation of wheels diameter, and are not robust to slippery conditions. This paper proposes an approach using static object detections from one or multiple low cost radars to improve the ego-motion estimation. First, the method determines a 2D motion of the ego vehicle for each radar available in the car. Three steps are applied: data association, linear regression, and outliers rejection. Then, the estimated 2D motions are fused with the proprioceptive sensors of the car in order to precisely estimate the speed and the yaw rate of the ego vehicle. In the tested scenarios, our approach drastically reduces the error speed with respect to classical wheel-based approach.
{"title":"Ego-Motion Estimation with Static Object Detections from Low Cost Radars","authors":"Romain Saussard, S. Zair, G. Gil","doi":"10.1109/ITSC.2018.8569738","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569738","url":null,"abstract":"Ego-motion estimation plays a crucial role in the achievement of autonomous driving and advanced driver assistance systems (ADAS). Classical approaches using wheel-based odometers suffers from errors due to the variation of wheels diameter, and are not robust to slippery conditions. This paper proposes an approach using static object detections from one or multiple low cost radars to improve the ego-motion estimation. First, the method determines a 2D motion of the ego vehicle for each radar available in the car. Three steps are applied: data association, linear regression, and outliers rejection. Then, the estimated 2D motions are fused with the proprioceptive sensors of the car in order to precisely estimate the speed and the yaw rate of the ego vehicle. In the tested scenarios, our approach drastically reduces the error speed with respect to classical wheel-based approach.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134346278","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 : 2018-11-01DOI: 10.1109/ITSC.2018.8569708
Oliver M. Winzer, Antonia S. Conti-Kufner, K. Bengler
The efficiency of traffic flow in urban areas can be improved with a Traffic Light Assistant (TLA), which indicates the status of upcoming traffic lights based on a driver's current traveling speed. Additionally, TLAs can help reduce the number of stops at traffic lights, which will also have a positive effect on fuel consumption. In the current paper, two different Human Machine Interfaces (HMIs) for a TLA were designed as smartphone applications with multimodal interactions. Driver glance behavior was tested according to the NHTSA guideline. The results show the outcomes of a suitability study carried out in a static driving simulator. Both HMI concepts (Perspective HMI: 915 ms; Two-Dimensional HMI: 849 ms) fulfill the NHTSA requirement that the 85th percentile of single glance duration is to less than 2 seconds.
{"title":"Intersection Traffic Light Assistant – An Evaluation of the Suitability of two Human Machine Interfaces","authors":"Oliver M. Winzer, Antonia S. Conti-Kufner, K. Bengler","doi":"10.1109/ITSC.2018.8569708","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569708","url":null,"abstract":"The efficiency of traffic flow in urban areas can be improved with a Traffic Light Assistant (TLA), which indicates the status of upcoming traffic lights based on a driver's current traveling speed. Additionally, TLAs can help reduce the number of stops at traffic lights, which will also have a positive effect on fuel consumption. In the current paper, two different Human Machine Interfaces (HMIs) for a TLA were designed as smartphone applications with multimodal interactions. Driver glance behavior was tested according to the NHTSA guideline. The results show the outcomes of a suitability study carried out in a static driving simulator. Both HMI concepts (Perspective HMI: 915 ms; Two-Dimensional HMI: 849 ms) fulfill the NHTSA requirement that the 85th percentile of single glance duration is to less than 2 seconds.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132270852","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 : 2018-11-01DOI: 10.1109/ITSC.2018.8569945
Martin Törngren, Xinhai Zhang, N. Mohan, Matthias Becker, Lars Svensson, Xin Tao, De-Jiu Chen, Jonas Westman
The complexity of automated driving poses challenges for providing safety assurance. Focusing on the architecting of an Autonomous Driving Intelligence (ADI), i.e. the computational intelligence, sensors and communication needed for high levels of automated driving, we investigate so called safety supervisors that complement the nominal functionality. We present a problem formulation and a functional architecture of a fault-tolerant ADI that encompasses a nominal and a safety supervisor channel. We then discuss the sources of hazardous events, the division of responsibilities among the channels, and when the supervisor should take over. We conclude with identified directions for further work.
{"title":"Architecting Safety Supervisors for High Levels of Automated Driving","authors":"Martin Törngren, Xinhai Zhang, N. Mohan, Matthias Becker, Lars Svensson, Xin Tao, De-Jiu Chen, Jonas Westman","doi":"10.1109/ITSC.2018.8569945","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569945","url":null,"abstract":"The complexity of automated driving poses challenges for providing safety assurance. Focusing on the architecting of an Autonomous Driving Intelligence (ADI), i.e. the computational intelligence, sensors and communication needed for high levels of automated driving, we investigate so called safety supervisors that complement the nominal functionality. We present a problem formulation and a functional architecture of a fault-tolerant ADI that encompasses a nominal and a safety supervisor channel. We then discuss the sources of hazardous events, the division of responsibilities among the channels, and when the supervisor should take over. We conclude with identified directions for further work.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132638037","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 : 2018-11-01DOI: 10.1109/ITSC.2018.8569768
Tuo Wang, J. Lv, Baiquan Wei, T. Tang, W. Shangguan
As a safety-critical system, the Chinese Train Control System Level 3 (CTCS-3) have complex fault modes. To generate test suite, which can cover all known faults, is very difficult. In this paper, we proposed a methodology of generating test suite for CTCS-3 train control system based on timed automata with input and output (TAIO) and mutation testing theory. Firstly, according to the characteristics of the fault modes in CTCS-3, mutation operators that contain all known faults of mode transition function (“change action”, “change target”, “change source”, etc) are designed. Secondly, the TAIO model of mode transition (TAIO A) in CTCS-3 is established. With each mutation operator, mutants of mode transition timed automata model (TAIO M) are built. Test suites are generated based on the conformance relation of TAIO A and TAIO M using the k-Bounded model checking technique. Finally, the coverage of the test suites have been analyzed by the conformance relation score (CRS), average conformance relation score (ACRS) and weighted conformance relation score (WCRS). Three fault modes of “change action”, “change invariant” and “sink location” could be covered effectively and completely detected, wherever, two fault modes coverage of “negate guard” and “invert reset” are not high enough, which need additional observation to detect.
{"title":"Test Suite Generation for CTCS-3 Train Control System Based On TAIO and Mutation Theory","authors":"Tuo Wang, J. Lv, Baiquan Wei, T. Tang, W. Shangguan","doi":"10.1109/ITSC.2018.8569768","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569768","url":null,"abstract":"As a safety-critical system, the Chinese Train Control System Level 3 (CTCS-3) have complex fault modes. To generate test suite, which can cover all known faults, is very difficult. In this paper, we proposed a methodology of generating test suite for CTCS-3 train control system based on timed automata with input and output (TAIO) and mutation testing theory. Firstly, according to the characteristics of the fault modes in CTCS-3, mutation operators that contain all known faults of mode transition function (“change action”, “change target”, “change source”, etc) are designed. Secondly, the TAIO model of mode transition (TAIO A) in CTCS-3 is established. With each mutation operator, mutants of mode transition timed automata model (TAIO M) are built. Test suites are generated based on the conformance relation of TAIO A and TAIO M using the k-Bounded model checking technique. Finally, the coverage of the test suites have been analyzed by the conformance relation score (CRS), average conformance relation score (ACRS) and weighted conformance relation score (WCRS). Three fault modes of “change action”, “change invariant” and “sink location” could be covered effectively and completely detected, wherever, two fault modes coverage of “negate guard” and “invert reset” are not high enough, which need additional observation to detect.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132867477","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 : 2018-11-01DOI: 10.1109/ITSC.2018.8569378
Nikola Karamanov, D. Andreev, M. Pfeifle, H. Bock, Mathias Otto, Matthias Schulze
Delivering an accurate representation of the lane ahead of an autonomously driving vehicle is one of the key functionalities of a good ADAS perception system. This statement holds especially for driving on a highway. Functions such as lane keeping assistance rely on a proper representation of the lanes from the perception subsystem. To achieve such a proper representation, information from various sensors such as cameras, LiDARs, radars, HD maps are taken into consideration. Up to now HD map information has been mainly used for longitudinal control, e.g. in cases when there are speed limits or curves ahead. Using map information for lateral control is more difficult, as the quality of the information derived from the map heavily depends on the precision of the positional and heading information of the ego vehicle. Uncertainty in the ego vehicle position and pose directly results in uncertainty in the line information retrieved from the digital map. We examine the influence of an uncertain Gaussian ego position and pose for the resulting map line information which is not necessarily Gaussian. In order to transport the map line information to other subsystems such as the lane fusion module, we need to approximate the map line distribution by a suitable data structure which is both accurate and compact. We discuss and evaluate suitable approximations of the resulting map line distributions such as mean values of map lines only, mean values combined with standard deviation values and mean values combined with the full covariance matrices. We show that the usage of mean values and covariance matrices approximate the true distributions rather accurately, and therefore are both from an accuracy point of view and from a bandwidth point of view the way to represent map lines in interfaces.
{"title":"Map Line Interface for Autonomous Driving","authors":"Nikola Karamanov, D. Andreev, M. Pfeifle, H. Bock, Mathias Otto, Matthias Schulze","doi":"10.1109/ITSC.2018.8569378","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569378","url":null,"abstract":"Delivering an accurate representation of the lane ahead of an autonomously driving vehicle is one of the key functionalities of a good ADAS perception system. This statement holds especially for driving on a highway. Functions such as lane keeping assistance rely on a proper representation of the lanes from the perception subsystem. To achieve such a proper representation, information from various sensors such as cameras, LiDARs, radars, HD maps are taken into consideration. Up to now HD map information has been mainly used for longitudinal control, e.g. in cases when there are speed limits or curves ahead. Using map information for lateral control is more difficult, as the quality of the information derived from the map heavily depends on the precision of the positional and heading information of the ego vehicle. Uncertainty in the ego vehicle position and pose directly results in uncertainty in the line information retrieved from the digital map. We examine the influence of an uncertain Gaussian ego position and pose for the resulting map line information which is not necessarily Gaussian. In order to transport the map line information to other subsystems such as the lane fusion module, we need to approximate the map line distribution by a suitable data structure which is both accurate and compact. We discuss and evaluate suitable approximations of the resulting map line distributions such as mean values of map lines only, mean values combined with standard deviation values and mean values combined with the full covariance matrices. We show that the usage of mean values and covariance matrices approximate the true distributions rather accurately, and therefore are both from an accuracy point of view and from a bandwidth point of view the way to represent map lines in interfaces.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122102244","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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