Pub Date : 2018-11-01DOI: 10.1109/ITSC.2018.8569522
Martin Bach, Daniel Stumper, K. Dietmayer
Robust traffic light detection and state recognition is of crucial importance on the path to automated vehicles. However, the mere classification of the signaled states does not suffice at complex multi-lane intersections. Rather, a complete understanding of the intersection, but at least the recognition of additional information (like arrows displayed on the traffic lights) is necessary. In this work, we developed a unified deep convolutional traffic light recognition system on the basis of the Faster R-CNN architecture, which is able to not only detect traffic lights and classify their state, but also distinguish their type (circle, straight, left, and right). An in-depth analysis of its performance on the large and diverse DriveU Traffic Light Dataset shows an overall detection performance of 0.92 Average Precision for traffic lights of width greater than 8 px. Additionally, other kinds of traffic lights, e.g. pedestrian lights, have been identified as main cause of false positives. Moreover, we evaluated the usefulness of the developed system to assess the traffic light states for all present driving directions revealing inconsistencies among multiple detections in single images.
鲁棒的交通灯检测和状态识别在自动驾驶道路上至关重要。然而，对于复杂的多车道交叉口，仅仅对信号状态进行分类是不够的。相反，完全了解十字路口，但至少识别额外的信息(如交通灯上显示的箭头)是必要的。在这项工作中，我们基于Faster R-CNN架构开发了一个统一的深度卷积交通灯识别系统，该系统不仅能够检测交通灯并对其状态进行分类，而且能够区分交通灯的类型(圆、直、左、右)。深入分析其在大型和多样化的DriveU交通灯数据集上的性能显示，对于宽度大于8像素的交通灯，其整体检测性能为0.92 Average Precision。此外，其他类型的交通信号灯，如行人信号灯，已被确定为误报的主要原因。此外，我们评估了开发的系统在评估所有当前驾驶方向的交通灯状态时的实用性，揭示了单个图像中多个检测之间的不一致性。
{"title":"Deep Convolutional Traffic Light Recognition for Automated Driving","authors":"Martin Bach, Daniel Stumper, K. Dietmayer","doi":"10.1109/ITSC.2018.8569522","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569522","url":null,"abstract":"Robust traffic light detection and state recognition is of crucial importance on the path to automated vehicles. However, the mere classification of the signaled states does not suffice at complex multi-lane intersections. Rather, a complete understanding of the intersection, but at least the recognition of additional information (like arrows displayed on the traffic lights) is necessary. In this work, we developed a unified deep convolutional traffic light recognition system on the basis of the Faster R-CNN architecture, which is able to not only detect traffic lights and classify their state, but also distinguish their type (circle, straight, left, and right). An in-depth analysis of its performance on the large and diverse DriveU Traffic Light Dataset shows an overall detection performance of 0.92 Average Precision for traffic lights of width greater than 8 px. Additionally, other kinds of traffic lights, e.g. pedestrian lights, have been identified as main cause of false positives. Moreover, we evaluated the usefulness of the developed system to assess the traffic light states for all present driving directions revealing inconsistencies among multiple detections in single images.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"59 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":"130861381","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.2018.8569912
D. Kypriadis, G. Pantziou, C. Konstantopoulos, D. Gavalas
This article presents algorithms for operator-based static repositioning in free-floating car-sharing systems (FFCSs) which derive cost-effective relocation tours. The FFCS fleet may include both electric and conventional cars. Car repositioning takes place overnight and aims at complete rebalancing of the system i.e., at achieving an optimal, based on user demand, distribution of cars among non-overlapping cells of the FFCS operating area. It is also combined with battery recharging of electric cars i.e., the level of battery power is taken into account when deciding if and where each vehicle will be relocated. The Minimum Walking Car Repositioning Problem (MWCRP) is solved whose main objective is to minimize the walking distance in the relocation tours as the walking part of a relocation tour is the most laborious, time-consuming and therefore, the most expensive part compared to the driving one. The MWCRP is extended to the $k$-MWCRP to handle the case that a team of $k > 1$ drivers is required to undertake the entire set of car relocations e.g., in case of a large fleet of cars and/or operating area. To the best of our knowledge this is the first vehicle repositioning approach aiming at minimizing the relocation cost by primarily minimizing the walking cost. Simulation results based on an FFCS operating in the cities of Rome and Florence, demonstrate the efficiency and the effectiveness of our approach.
{"title":"Minimum Walking Static Repositioning in Free-Floating Electric Car-Sharing Systems","authors":"D. Kypriadis, G. Pantziou, C. Konstantopoulos, D. Gavalas","doi":"10.1109/ITSC.2018.8569912","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569912","url":null,"abstract":"This article presents algorithms for operator-based static repositioning in free-floating car-sharing systems (FFCSs) which derive cost-effective relocation tours. The FFCS fleet may include both electric and conventional cars. Car repositioning takes place overnight and aims at complete rebalancing of the system i.e., at achieving an optimal, based on user demand, distribution of cars among non-overlapping cells of the FFCS operating area. It is also combined with battery recharging of electric cars i.e., the level of battery power is taken into account when deciding if and where each vehicle will be relocated. The Minimum Walking Car Repositioning Problem (MWCRP) is solved whose main objective is to minimize the walking distance in the relocation tours as the walking part of a relocation tour is the most laborious, time-consuming and therefore, the most expensive part compared to the driving one. The MWCRP is extended to the $k$-MWCRP to handle the case that a team of $k > 1$ drivers is required to undertake the entire set of car relocations e.g., in case of a large fleet of cars and/or operating area. To the best of our knowledge this is the first vehicle repositioning approach aiming at minimizing the relocation cost by primarily minimizing the walking cost. Simulation results based on an FFCS operating in the cities of Rome and Florence, demonstrate the efficiency and the effectiveness of our approach.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"12 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":"130445701","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.2018.8569662
Banishree Ghosh, Yuanzheng Zhu, J. Dauwels
The variable message signs, abbreviated as VMS messages, are disseminated through LED displays to provide the travelers and motorists information, warning and guidance on the current traffic situation. As an advanced traffic guidance system, the VMS messages help drivers to choose the routes with lower traffic volumes. Thus, the vehicles can be more evenly distributed in the road network to improve the performance of traffic system and reduce traffic delays. To this end, the VMS technology has been widely used in the expressways of Singapore. This paper aims to evaluate the immediate impact of VMS on the overall traffic distribution of Singapore in response to accidents and obstacles. For this purpose, we consider the incidents data and their corresponding VMS messages from the two busiest expressways of Singapore, namely Pan Island Expressway (PIE) and Central Expressway (CTE). For this analysis, we ignore the VMS messages of the locations which are already congested due to traffic incidents, since the motorists may be influenced by the congestion and not entirely by the VMS messages. The next step is to obtain the locations of other VMS displays and their nearby downstream exit points. The central argument of this analysis is that if the average traffic flow of the exits increases significantly compared to that of normal days, it demonstrates the impact of the VMS messages on the drivers' behavior. Our results show that the average outgoing traffic flow from the expressways towards the exits increases by 14% after the VMS messages have been activated.
{"title":"Effectiveness of VMS Messages in Influencing the Motorists' Travel Behaviour","authors":"Banishree Ghosh, Yuanzheng Zhu, J. Dauwels","doi":"10.1109/ITSC.2018.8569662","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569662","url":null,"abstract":"The variable message signs, abbreviated as VMS messages, are disseminated through LED displays to provide the travelers and motorists information, warning and guidance on the current traffic situation. As an advanced traffic guidance system, the VMS messages help drivers to choose the routes with lower traffic volumes. Thus, the vehicles can be more evenly distributed in the road network to improve the performance of traffic system and reduce traffic delays. To this end, the VMS technology has been widely used in the expressways of Singapore. This paper aims to evaluate the immediate impact of VMS on the overall traffic distribution of Singapore in response to accidents and obstacles. For this purpose, we consider the incidents data and their corresponding VMS messages from the two busiest expressways of Singapore, namely Pan Island Expressway (PIE) and Central Expressway (CTE). For this analysis, we ignore the VMS messages of the locations which are already congested due to traffic incidents, since the motorists may be influenced by the congestion and not entirely by the VMS messages. The next step is to obtain the locations of other VMS displays and their nearby downstream exit points. The central argument of this analysis is that if the average traffic flow of the exits increases significantly compared to that of normal days, it demonstrates the impact of the VMS messages on the drivers' behavior. Our results show that the average outgoing traffic flow from the expressways towards the exits increases by 14% after the VMS messages have been activated.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"2 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":"127849328","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.2018.8569315
Kanika Bathla, V. Raychoudhury, Divya Saxena, A. Kshemkalyani
Taxicabs play an important role in urban public transportation. Analyzing taxi traffic of Shanghai, San Francisco, and New York City, we have found that the short trips within city are mostly of commuters during office hours and span a specific city area. Now, if the large number of commuters are ready to share their rides, that will have a huge impact on the ‘super-commute’ problem faced in various cities of USA and around the world. While ride-sharing can increase taxi occupancy and profit for drivers and savings for passengers, it reduces the overall on-road traffic and thereby the average commute time and carbon foot-print. While centralized ride-sharing services, like car-pooling, can address the problem to some extent, they lack scalability and power to dynamically adapt the taxi schedule for best results. In this paper, we propose a four-way model for the ride-sharing problem and develop a novel distributed taxi ride sharing (TRS) algorithm to address dynamic scheduling of ride sharing requests. Our algorithm shows the overall reduction in total distance travelled by taxis as a result of ride sharing. Empirical results using large scale taxi GPS traces from Shanghai, China show that TRS algorithm can grossly outperform a Taxi Distance Minimization (TDM) algorithm. TRS accommodates 33% higher ride share among passengers while dealing with 44,241 requests handled by 4,000 taxis on a single day in Shanghai.
{"title":"Real-Time Distributed Taxi Ride Sharing","authors":"Kanika Bathla, V. Raychoudhury, Divya Saxena, A. Kshemkalyani","doi":"10.1109/ITSC.2018.8569315","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569315","url":null,"abstract":"Taxicabs play an important role in urban public transportation. Analyzing taxi traffic of Shanghai, San Francisco, and New York City, we have found that the short trips within city are mostly of commuters during office hours and span a specific city area. Now, if the large number of commuters are ready to share their rides, that will have a huge impact on the ‘super-commute’ problem faced in various cities of USA and around the world. While ride-sharing can increase taxi occupancy and profit for drivers and savings for passengers, it reduces the overall on-road traffic and thereby the average commute time and carbon foot-print. While centralized ride-sharing services, like car-pooling, can address the problem to some extent, they lack scalability and power to dynamically adapt the taxi schedule for best results. In this paper, we propose a four-way model for the ride-sharing problem and develop a novel distributed taxi ride sharing (TRS) algorithm to address dynamic scheduling of ride sharing requests. Our algorithm shows the overall reduction in total distance travelled by taxis as a result of ride sharing. Empirical results using large scale taxi GPS traces from Shanghai, China show that TRS algorithm can grossly outperform a Taxi Distance Minimization (TDM) algorithm. TRS accommodates 33% higher ride share among passengers while dealing with 44,241 requests handled by 4,000 taxis on a single day in Shanghai.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"32 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":"131312817","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.2018.8569823
A. Vanzella, G. Zat, H. Scherer, L. L. Alves, Diego J. Fraga
The idea of sharing resources is a world tendency that is spreading quickly because people's behavior is continually changing due to a lot of variables, such as easy access to technology. Some examples that can illustrate this transformation are: Airbnb, Couchsurfing and Uber. Taking a look from a perspective that comprises both economy and environmental issues, vehicle sharing is a concept that brings a lot of easiness and benefits for those who used as a sustainable means of locomotion. This paper aims to contribute with a sustainable vision already adopted by Itaipu Binacional, the most important hydroelectric power plant in Brazil. To achieve this, a carsharing platform (MoVE) was developed to include electric vehicles to be used by Itaipu employees. Trying to achieve high scalability, MoVE platform offers an API that allows communication with any hardware that is capable of collecting vehicle data.
{"title":"MoVE: Test Case for Electric Carsharing at Itaipu","authors":"A. Vanzella, G. Zat, H. Scherer, L. L. Alves, Diego J. Fraga","doi":"10.1109/ITSC.2018.8569823","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569823","url":null,"abstract":"The idea of sharing resources is a world tendency that is spreading quickly because people's behavior is continually changing due to a lot of variables, such as easy access to technology. Some examples that can illustrate this transformation are: Airbnb, Couchsurfing and Uber. Taking a look from a perspective that comprises both economy and environmental issues, vehicle sharing is a concept that brings a lot of easiness and benefits for those who used as a sustainable means of locomotion. This paper aims to contribute with a sustainable vision already adopted by Itaipu Binacional, the most important hydroelectric power plant in Brazil. To achieve this, a carsharing platform (MoVE) was developed to include electric vehicles to be used by Itaipu employees. Trying to achieve high scalability, MoVE platform offers an API that allows communication with any hardware that is capable of collecting vehicle data.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"2 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":"129079347","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.2018.8569759
Hanwool Woo, Yonghoon Ji, Y. Tamura, Yasuhide Kuroda, Takashi Sugano, Yasunori Yamamoto, A. Yamashita, H. Asama
In this study, we propose the advanced adaptive cruise control for assessing the collision risk with surrounding vehicles and control the ego vehicle as a way to improve driving safety. Autonomous driving and advanced driver assistance systems (ADAS) have attracted attention as solutions to accident prevention. The ability to anticipate a situation and automatically control a maneuver to avoid a collision is expected to become a reality in the near future. Our research group has focused on the requirements of such ability, particularly lane changing, which is the main factor of traffic accidents. The advanced adaptive cruise control adjusts its distance from surrounding vehicles to minimize a collision risk in advance. The proposed method estimates the intentions of the surrounding traffic participants and predicts their future actions. Based on such prediction, a collision risk assessment is performed. It was demonstrated that the proposed control method can dramatically improve driving safety over human drivers.
{"title":"Advanced Adaptive Cruise Control Based on Collision Risk Assessment","authors":"Hanwool Woo, Yonghoon Ji, Y. Tamura, Yasuhide Kuroda, Takashi Sugano, Yasunori Yamamoto, A. Yamashita, H. Asama","doi":"10.1109/ITSC.2018.8569759","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569759","url":null,"abstract":"In this study, we propose the advanced adaptive cruise control for assessing the collision risk with surrounding vehicles and control the ego vehicle as a way to improve driving safety. Autonomous driving and advanced driver assistance systems (ADAS) have attracted attention as solutions to accident prevention. The ability to anticipate a situation and automatically control a maneuver to avoid a collision is expected to become a reality in the near future. Our research group has focused on the requirements of such ability, particularly lane changing, which is the main factor of traffic accidents. The advanced adaptive cruise control adjusts its distance from surrounding vehicles to minimize a collision risk in advance. The proposed method estimates the intentions of the surrounding traffic participants and predicts their future actions. Based on such prediction, a collision risk assessment is performed. It was demonstrated that the proposed control method can dramatically improve driving safety over human drivers.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"58 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":"128857721","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.2018.8569482
Giammarco Valenti, L. Pascali, F. Biral
This paper deals with the problem of the prediction of driver intention. The problem is relevant in the context of modern Advanced Driver Assistance Systems. More specifically, we address the task to continuously generate a predicted longitudinal velocity profile with a fixed time horizon and associated with a driver's intention (e.g. overtake). The objective is to obtain a “general purpose” prediction, aimed to feed any ADAS algorithm requiring future longitudinal velocity and intention informations, like safety applications, warning systems or MPC-based algorithms. The prediction makes use of the artificial co-driver concept, which is here designed to deal with longitudinal inputs only. The co-driver is an agent able to perform inference of intention by means of a mirroring approach, trying to imitate the human driving behavior. The approach is conceived to be simple and modular, using only longitudinal informations from the vehicle, and flexible to the availability of external informations (e.g. vehicle ahead). The works includes the implementation of a jerk filtering technique proposed by some of the authors, this technique is used in a mirroring approach for the first time. Preliminary results on prediction are presented, and future development and validation are discussed.
{"title":"Estimation of longitudinal speed profile of car drivers via bio-inspired mirroring mechanism","authors":"Giammarco Valenti, L. Pascali, F. Biral","doi":"10.1109/ITSC.2018.8569482","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569482","url":null,"abstract":"This paper deals with the problem of the prediction of driver intention. The problem is relevant in the context of modern Advanced Driver Assistance Systems. More specifically, we address the task to continuously generate a predicted longitudinal velocity profile with a fixed time horizon and associated with a driver's intention (e.g. overtake). The objective is to obtain a “general purpose” prediction, aimed to feed any ADAS algorithm requiring future longitudinal velocity and intention informations, like safety applications, warning systems or MPC-based algorithms. The prediction makes use of the artificial co-driver concept, which is here designed to deal with longitudinal inputs only. The co-driver is an agent able to perform inference of intention by means of a mirroring approach, trying to imitate the human driving behavior. The approach is conceived to be simple and modular, using only longitudinal informations from the vehicle, and flexible to the availability of external informations (e.g. vehicle ahead). The works includes the implementation of a jerk filtering technique proposed by some of the authors, this technique is used in a mirroring approach for the first time. Preliminary results on prediction are presented, and future development and validation are discussed.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"45 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":"125503178","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.2018.8569288
Jiateng Yin, T. Tang, Lixing Yang, J. Xun, S. Su, Yihui Wang
Considering the uncertain characteristics of disruptions and passenger demand in a metro line, this study develops a two-stage stochastic optimization model that uses backup trains in the storage line to reschedule the timetable and evacuate the delayed passengers caused by the disruption. Specifically, the first stage model determines the optimal allocation plan of backup trains in the storage lines, which aims to achieve a trade-off between investment cost of using backup trains and the expected total travel time of delayed passengers across different stochastic scenarios. The second stage optimizes the timetable of delayed trains on the tracks and backup trains at the storage line in order to minimize the passenger travel time under each stochastic scenario. In particular, the second-stage model is formulated as a multi-commodity network flow model, by which the train capacity can be handled by setting appropriate arc capacity constraints. Numerical experiments based on the historical data in Beijing Subway verify the effectiveness of the proposed approach to reduce the passenger delay time.
{"title":"A Two-Stage Stochastic Optimization Model for Passenger-Oriented Metro Rescheduling with Backup Trains","authors":"Jiateng Yin, T. Tang, Lixing Yang, J. Xun, S. Su, Yihui Wang","doi":"10.1109/ITSC.2018.8569288","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569288","url":null,"abstract":"Considering the uncertain characteristics of disruptions and passenger demand in a metro line, this study develops a two-stage stochastic optimization model that uses backup trains in the storage line to reschedule the timetable and evacuate the delayed passengers caused by the disruption. Specifically, the first stage model determines the optimal allocation plan of backup trains in the storage lines, which aims to achieve a trade-off between investment cost of using backup trains and the expected total travel time of delayed passengers across different stochastic scenarios. The second stage optimizes the timetable of delayed trains on the tracks and backup trains at the storage line in order to minimize the passenger travel time under each stochastic scenario. In particular, the second-stage model is formulated as a multi-commodity network flow model, by which the train capacity can be handled by setting appropriate arc capacity constraints. Numerical experiments based on the historical data in Beijing Subway verify the effectiveness of the proposed approach to reduce the passenger delay time.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"82 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":"125560078","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.2018.8569764
Alexander Carballo, E. Takeuchi, K. Takeda
In this work we propose a new method to create high density ground maps for autonomous vehicles based on low boundary elevation estimation. Ground maps are created using ray casting of laser beams in 3D LiDAR data accumulated over time and computing the lower elevation data between 3D LiDAR scans. This lower boundary works as a low envelope of the accumulated point cloud. Our lower boundary ground maps approach is not affected by moving objects on the road, and produces high density maps even with coarse vertical resolution 3D LiDARs, works on long curved roads with different elevations, and it is computationally efficient. As proof of concept, we present an application for real-time obstacle and ground segmentation of 3D LiDAR data using our ground maps.
{"title":"High Density Ground Maps using Low Boundary Height Estimation for Autonomous Vehicles","authors":"Alexander Carballo, E. Takeuchi, K. Takeda","doi":"10.1109/ITSC.2018.8569764","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569764","url":null,"abstract":"In this work we propose a new method to create high density ground maps for autonomous vehicles based on low boundary elevation estimation. Ground maps are created using ray casting of laser beams in 3D LiDAR data accumulated over time and computing the lower elevation data between 3D LiDAR scans. This lower boundary works as a low envelope of the accumulated point cloud. Our lower boundary ground maps approach is not affected by moving objects on the road, and produces high density maps even with coarse vertical resolution 3D LiDARs, works on long curved roads with different elevations, and it is computationally efficient. As proof of concept, we present an application for real-time obstacle and ground segmentation of 3D LiDAR data using our ground maps.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"14 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":"126669157","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.2018.8569417
Mohammed Elhenawy, A. Bond, A. Rakotonirainy
Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. Evaluation of their safety benefits in Field Operational Tests (FOT) is needed to demonstrate its benefits and build public awareness and uptake. The result of the evaluation can tell us if the C-ITS algorithms that trigger the safety events, and consequently the Human-Machine Interface (HMI) messages, are appropriately fine-tuned to induce the expected driver behavior change formulated as hypothesis. In this paper we will introduce the safety hypotheses for seven driver safety use cases being deployed by the Queensland Department of Transport and Main Roads in the Ipswich Connected Vehicle Pilot in Australia. The safety performance indicators to test these hypotheses will be introduced as well. The main challenge in evaluating the safety benefits is only using the information collected from C-ITS units without augmentation from any other sensors. To validate data collection, we ran an experiment at the Mt Cotton training facility close to Brisbane and collected Cooperative Awareness Message (CAM) messages to analyze them and check whether the speed and acceleration information extracted from them is accurate enough to detect change in speed and braking. The analysis results show that we can detect the change in speed, but the acceleration/braking pattern is very noisy and needs careful manipulation to retrieve the braking behavior.
{"title":"C-ITS Safety Evaluation Methodology based on Cooperative Awareness Messages","authors":"Mohammed Elhenawy, A. Bond, A. Rakotonirainy","doi":"10.1109/ITSC.2018.8569417","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569417","url":null,"abstract":"Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. Evaluation of their safety benefits in Field Operational Tests (FOT) is needed to demonstrate its benefits and build public awareness and uptake. The result of the evaluation can tell us if the C-ITS algorithms that trigger the safety events, and consequently the Human-Machine Interface (HMI) messages, are appropriately fine-tuned to induce the expected driver behavior change formulated as hypothesis. In this paper we will introduce the safety hypotheses for seven driver safety use cases being deployed by the Queensland Department of Transport and Main Roads in the Ipswich Connected Vehicle Pilot in Australia. The safety performance indicators to test these hypotheses will be introduced as well. The main challenge in evaluating the safety benefits is only using the information collected from C-ITS units without augmentation from any other sensors. To validate data collection, we ran an experiment at the Mt Cotton training facility close to Brisbane and collected Cooperative Awareness Message (CAM) messages to analyze them and check whether the speed and acceleration information extracted from them is accurate enough to detect change in speed and braking. The analysis results show that we can detect the change in speed, but the acceleration/braking pattern is very noisy and needs careful manipulation to retrieve the braking behavior.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"27 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":"126745071","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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