Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294505
Daniel Vriesman, Bernhard Thoresz, Dagmar Steinhauser, A. Zimmer, A. Britto, T. Brandmeier
Performing high level autonomous navigation in a reliable and robust way considering different ambient conditions is a very challenging task. To achieve this goal, a mix of different sensors, such as cameras, lidars, and radars, are normally used to gather information from the environment. Since each sensor works based on different physical principles, they are affected differently by the challenging conditions, like weather interference for example. Looking to explore the influence of high intensity rain (98mm/h), this paper presents a robust experimental protocol that analyzes the influence inside the near field of lidar and radar sensors. The results shows how the effect of rain droplets degrades the backscattering signal from both sensors, affecting the information regarding the target’s dimension. The consequences in terms object and feature detection’es changes are also discussed.
{"title":"An Experimental Analysis of Rain Interference on Detection and Ranging Sensors","authors":"Daniel Vriesman, Bernhard Thoresz, Dagmar Steinhauser, A. Zimmer, A. Britto, T. Brandmeier","doi":"10.1109/ITSC45102.2020.9294505","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294505","url":null,"abstract":"Performing high level autonomous navigation in a reliable and robust way considering different ambient conditions is a very challenging task. To achieve this goal, a mix of different sensors, such as cameras, lidars, and radars, are normally used to gather information from the environment. Since each sensor works based on different physical principles, they are affected differently by the challenging conditions, like weather interference for example. Looking to explore the influence of high intensity rain (98mm/h), this paper presents a robust experimental protocol that analyzes the influence inside the near field of lidar and radar sensors. The results shows how the effect of rain droplets degrades the backscattering signal from both sensors, affecting the information regarding the target’s dimension. The consequences in terms object and feature detection’es changes are also discussed.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133757668","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294224
Anis Sellaouti, O. Arslan, S. Hoffmann
Since mid-June 2019, electric scooters have been permitted on German roads. Many companies offer these in the form of sharing vehicles in Germany's major cities. These justify their existence through the zero-emission alternative to cars. How these vehicles are accepted, how they are used and whether they actually contribute to the transformation of the German traffic is being analysed in this paper.An online survey in Munich shows that e-scooters mainly replace walking and public transport and despite their large presence in the city landscape they are not used often. It appears that e-scooters are perceived as a leisure/fun object and less safe than bikes. The introduction of parking spaces with integrated charging facilities could save the bad reputation of electric scooters as deduced in the study. This reputation covers the environment, safety and the cityscape. In this study is also shown how the pricing model could be traced back to the absence of first mile last mile (FMLM) using.
{"title":"Analysis of the use or non-use of e-scooters, their integration in the city of Munich (Germany) and their potential as an additional mobility system","authors":"Anis Sellaouti, O. Arslan, S. Hoffmann","doi":"10.1109/ITSC45102.2020.9294224","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294224","url":null,"abstract":"Since mid-June 2019, electric scooters have been permitted on German roads. Many companies offer these in the form of sharing vehicles in Germany's major cities. These justify their existence through the zero-emission alternative to cars. How these vehicles are accepted, how they are used and whether they actually contribute to the transformation of the German traffic is being analysed in this paper.An online survey in Munich shows that e-scooters mainly replace walking and public transport and despite their large presence in the city landscape they are not used often. It appears that e-scooters are perceived as a leisure/fun object and less safe than bikes. The introduction of parking spaces with integrated charging facilities could save the bad reputation of electric scooters as deduced in the study. This reputation covers the environment, safety and the cityscape. In this study is also shown how the pricing model could be traced back to the absence of first mile last mile (FMLM) using.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115574433","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294249
Yaoting Huang, Wenlian Lu
Contemporary electronic marketing leads to massive requirements of courier services in China. A local outlet providing delivery and real-time pickup services, however, severely depends on the good staff of experience to handle the routing tasks. These routing tasks are formulated as a one-to-many-to-one dynamic pickup and delivery problem.In this research, we have developed an online method to solve routing problems. With adaptive memory and heuristic insertion for a speedy response, this method generates results by taking both quality and responsiveness into account, based on simulated annealing to optimize untraveled routes during the trip. This real-time method enables to establish a real-time route planning system: after initialization, adaptive memory is built up to contain the multiple candidate solutions and updated by real-time optimization responding to real-time requests insertion; once a dispatching order is needed, the best solution from the adaptive memory will be selected.By testing on simulated data with different dynamism level, we have gained good results of both better responsiveness and quality than that of the greedy algorithm, and showing that data with high dynamism can also have low-cost solutions. This work contributes to reducing human involvement in real-time courier service.
{"title":"Online Parallel optimization Approach to Courier Routing Problems*","authors":"Yaoting Huang, Wenlian Lu","doi":"10.1109/ITSC45102.2020.9294249","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294249","url":null,"abstract":"Contemporary electronic marketing leads to massive requirements of courier services in China. A local outlet providing delivery and real-time pickup services, however, severely depends on the good staff of experience to handle the routing tasks. These routing tasks are formulated as a one-to-many-to-one dynamic pickup and delivery problem.In this research, we have developed an online method to solve routing problems. With adaptive memory and heuristic insertion for a speedy response, this method generates results by taking both quality and responsiveness into account, based on simulated annealing to optimize untraveled routes during the trip. This real-time method enables to establish a real-time route planning system: after initialization, adaptive memory is built up to contain the multiple candidate solutions and updated by real-time optimization responding to real-time requests insertion; once a dispatching order is needed, the best solution from the adaptive memory will be selected.By testing on simulated data with different dynamism level, we have gained good results of both better responsiveness and quality than that of the greedy algorithm, and showing that data with high dynamism can also have low-cost solutions. This work contributes to reducing human involvement in real-time courier service.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115851137","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294628
Miaohang Hu, N. Bhouri
This article uses primarily the Grey Relational analysis method to analyze the effectiveness of 14 indicators related to transportation network resilience. In the process of analysis, we use the indicator data obtained from an unattacked network as the optimal reference sequence and a network attacked on the most connected node as the worst reference sequence. Besides the optimal and the worst scenarios, to study the network resilience, we define a network attacking strategy consisting in an assault on one node at a time, orderly for all nodes of the network. A relative Grey Correlation Degree is also proposed to evaluate the results. The analysis is made on 10 public transport networks. They show that the Global Efficiency is the indicator that has the greatest influence on the resilience of the public transportation network. We also categorized the resilience indicators into three different groups. We find that the most important category for network resilience is the Network Efficiency indicator, which includes the network structure plus the bus travel time.
{"title":"Evaluation of Resilience Indicators for Public Transportation Networks by the Grey Relational Analysis","authors":"Miaohang Hu, N. Bhouri","doi":"10.1109/ITSC45102.2020.9294628","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294628","url":null,"abstract":"This article uses primarily the Grey Relational analysis method to analyze the effectiveness of 14 indicators related to transportation network resilience. In the process of analysis, we use the indicator data obtained from an unattacked network as the optimal reference sequence and a network attacked on the most connected node as the worst reference sequence. Besides the optimal and the worst scenarios, to study the network resilience, we define a network attacking strategy consisting in an assault on one node at a time, orderly for all nodes of the network. A relative Grey Correlation Degree is also proposed to evaluate the results. The analysis is made on 10 public transport networks. They show that the Global Efficiency is the indicator that has the greatest influence on the resilience of the public transportation network. We also categorized the resilience indicators into three different groups. We find that the most important category for network resilience is the Network Efficiency indicator, which includes the network structure plus the bus travel time.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124570919","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294414
Qichao Wang, M. Abbas
In our previous work, we proposed a Virtual Phase-Link street traffic model to provide optimal control of green splits. The simulations implemented the offsets which were obtained from Vistro offline. Offsets can significantly impact the performance of arterial traffic controls. This paper introduces offsets as optimization variables to the Virtual Phase-Link street traffic model. Based on the optimization results from the optimal green splits control proposed in the previous work, we derived the delay function for offsets optimization. The proposed offsets optimization were tested under two scenarios of the same arterial against their base cases in simulations. It was found that in both scenarios, the proposed method resulted in significantly less delay compared to the base cases. It was also found that the proposed offsets optimization method can identify the dominant traffic path and provide progression optimization for it.
{"title":"Introducing Offsets to the Virtual Phase-link Street Traffic Model for Arterial Traffic Control","authors":"Qichao Wang, M. Abbas","doi":"10.1109/ITSC45102.2020.9294414","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294414","url":null,"abstract":"In our previous work, we proposed a Virtual Phase-Link street traffic model to provide optimal control of green splits. The simulations implemented the offsets which were obtained from Vistro offline. Offsets can significantly impact the performance of arterial traffic controls. This paper introduces offsets as optimization variables to the Virtual Phase-Link street traffic model. Based on the optimization results from the optimal green splits control proposed in the previous work, we derived the delay function for offsets optimization. The proposed offsets optimization were tested under two scenarios of the same arterial against their base cases in simulations. It was found that in both scenarios, the proposed method resulted in significantly less delay compared to the base cases. It was also found that the proposed offsets optimization method can identify the dominant traffic path and provide progression optimization for it.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116887132","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294281
Tomoki Nishi, Keisuke Otaki, Ayano Okoso, A. Fukunaga
On-demand mobile facility services are a promising approach to mitigate social problems related to transportation. Route optimization to satisfy customer demands is an essential technology to realize the services. Most studies of the route optimization for the services have been focused on finding a better assignment from vehicles to customers and a better order of visiting customer locations under the assumption that the customers waiting at the locations without moving. In this paper, we formulate cooperative routing problem between customers and vehicles, which minimizes total travel cost by optimizing both vehicle and customer routes. We also propose a heuristic approach to find solutions for large instances. We demonstrate that customer cooperation helps to reduce the total travel cost compared to a solution of standard vehicle routing problem in synthetic experiments using the road network of Manhattan, NY, USA. We confirmed that the total travel cost of the customers and the vehicles was reduced by 20% using our heuristics comparing to solutions of the vehicle routing problem with little extra computational cost.
{"title":"Cooperative Routing Problem between Customers and Vehicles for On-demand Mobile Facility Services","authors":"Tomoki Nishi, Keisuke Otaki, Ayano Okoso, A. Fukunaga","doi":"10.1109/ITSC45102.2020.9294281","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294281","url":null,"abstract":"On-demand mobile facility services are a promising approach to mitigate social problems related to transportation. Route optimization to satisfy customer demands is an essential technology to realize the services. Most studies of the route optimization for the services have been focused on finding a better assignment from vehicles to customers and a better order of visiting customer locations under the assumption that the customers waiting at the locations without moving. In this paper, we formulate cooperative routing problem between customers and vehicles, which minimizes total travel cost by optimizing both vehicle and customer routes. We also propose a heuristic approach to find solutions for large instances. We demonstrate that customer cooperation helps to reduce the total travel cost compared to a solution of standard vehicle routing problem in synthetic experiments using the road network of Manhattan, NY, USA. We confirmed that the total travel cost of the customers and the vehicles was reduced by 20% using our heuristics comparing to solutions of the vehicle routing problem with little extra computational cost.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116900154","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294448
C. Menelaou, S. Timotheou, P. Kolios, C. Panayiotou
In this paper, we jointly integrate route-reservations with a pricing mechanism to evaluate the effect of congestion pricing on the driver departure time choices. Route-reservations have shown to be a durable congestion mitigation mechanism that can achieve up to 70% reduction in travel times. Unfortunately, this improvement is achieved only when the majority of the drivers comply with the suggested routes and departure times. Therefore, a pricing mechanism is proposed that allows drivers to deviate from the suggested departure times. To identify the departure time choices of drivers we explicitly take into account their desired departure time from their origin and also the start time of the activity they are planning to perform. The proposed flexible route-reservation framework is evaluated in a microscopic simulation with results demonstrating how the introduced pricing mechanism can eliminate congestion while allowing flexibility to drivers to deviated from the suggested departure time.
{"title":"Flexible Route-Reservations through Pricing","authors":"C. Menelaou, S. Timotheou, P. Kolios, C. Panayiotou","doi":"10.1109/ITSC45102.2020.9294448","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294448","url":null,"abstract":"In this paper, we jointly integrate route-reservations with a pricing mechanism to evaluate the effect of congestion pricing on the driver departure time choices. Route-reservations have shown to be a durable congestion mitigation mechanism that can achieve up to 70% reduction in travel times. Unfortunately, this improvement is achieved only when the majority of the drivers comply with the suggested routes and departure times. Therefore, a pricing mechanism is proposed that allows drivers to deviate from the suggested departure times. To identify the departure time choices of drivers we explicitly take into account their desired departure time from their origin and also the start time of the activity they are planning to perform. The proposed flexible route-reservation framework is evaluated in a microscopic simulation with results demonstrating how the introduced pricing mechanism can eliminate congestion while allowing flexibility to drivers to deviated from the suggested departure time.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123409721","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294345
Iljoo Baek, Tzu Chieh Tai, Manoj Bhat, Karun Ellango, Tarang Shah, Kamal Fuseini, R. Rajkumar
Reliable curb detection is critical for safe autonomous driving in urban contexts. Curb detection and tracking are also useful in vehicle localization and path planning. Past work utilized a 3D LiDAR sensor to determine accurate distance information and the geometric attributes of curbs. However, such an approach requires dense point cloud data and is also vulnerable to false positives from obstacles present on both road and off-road areas. In this paper, we propose an approach to detect and track curbs by fusing together data from multiple sensors: sparse LiDAR data, a mono camera and low-cost ultrasonic sensors. The detection algorithm is based on a single 3D LiDAR and a mono camera sensor used to detect candidate curb features and it effectively removes false positives arising from surrounding static and moving obstacles. The detection accuracy of the tracking algorithm is boosted by using Kalman filter-based prediction and fusion with lateral distance information from low-cost ultrasonic sensors. We next propose a line-fitting algorithm that yields robust results for curb locations. Finally, we demonstrate the practical feasibility of our solution by testing in different road environments and evaluating our implementation in a real vehicle1. Our algorithm maintains over 90% accuracy within 4.5-22 meters and 0-14 meters for the KITTI dataset and our dataset respectively, and its average processing time per frame is approximately 10 ms on Intel i7 x86 and 100ms on NVIDIA Xavier board.
{"title":"CurbScan: Curb Detection and Tracking Using Multi-Sensor Fusion","authors":"Iljoo Baek, Tzu Chieh Tai, Manoj Bhat, Karun Ellango, Tarang Shah, Kamal Fuseini, R. Rajkumar","doi":"10.1109/ITSC45102.2020.9294345","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294345","url":null,"abstract":"Reliable curb detection is critical for safe autonomous driving in urban contexts. Curb detection and tracking are also useful in vehicle localization and path planning. Past work utilized a 3D LiDAR sensor to determine accurate distance information and the geometric attributes of curbs. However, such an approach requires dense point cloud data and is also vulnerable to false positives from obstacles present on both road and off-road areas. In this paper, we propose an approach to detect and track curbs by fusing together data from multiple sensors: sparse LiDAR data, a mono camera and low-cost ultrasonic sensors. The detection algorithm is based on a single 3D LiDAR and a mono camera sensor used to detect candidate curb features and it effectively removes false positives arising from surrounding static and moving obstacles. The detection accuracy of the tracking algorithm is boosted by using Kalman filter-based prediction and fusion with lateral distance information from low-cost ultrasonic sensors. We next propose a line-fitting algorithm that yields robust results for curb locations. Finally, we demonstrate the practical feasibility of our solution by testing in different road environments and evaluating our implementation in a real vehicle1. Our algorithm maintains over 90% accuracy within 4.5-22 meters and 0-14 meters for the KITTI dataset and our dataset respectively, and its average processing time per frame is approximately 10 ms on Intel i7 x86 and 100ms on NVIDIA Xavier board.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"28 3-4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123583002","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294547
R. Yu, Haoan Ai, Zhenqi Gao
High risk driving scenarios are critical for the deployment of highly automated vehicles virtual test. In this study, we have proposed a deep learning method to identify high risk scenarios from the field operation test (FOT) data. The proposed method tries to overcome the shortcomings of existing relevant studies for their limited utilizations of video data and mainly based upon instant kinematic indicators, which has led to high false alarm rate issue. In this study, a combined video analysis method (Convolutional Neural Network, CNN) and temporal feature analysis model (Long Short-Term Memory, LSTM) was proposed. To be specific, we used CNN-LSTM and Convolutional Neural Networks and Long Short-Term Memory (Resnet-LSTM) to perform the classifications for high risk scenarios and non-conflict scenarios. The empirical analyses have been conducted using commercial vehicle FOT data. And the results showed that the overall model performance (AUC index) in the test set could reach 0.91 with 83% accuracy rate. Finally, the future works have been discussed from the aspects of further extractions of video data and investigations of LSTM modelling results.
{"title":"Identifying High Risk Driving Scenarios Utilizing a CNN-LSTM Analysis Approach*","authors":"R. Yu, Haoan Ai, Zhenqi Gao","doi":"10.1109/ITSC45102.2020.9294547","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294547","url":null,"abstract":"High risk driving scenarios are critical for the deployment of highly automated vehicles virtual test. In this study, we have proposed a deep learning method to identify high risk scenarios from the field operation test (FOT) data. The proposed method tries to overcome the shortcomings of existing relevant studies for their limited utilizations of video data and mainly based upon instant kinematic indicators, which has led to high false alarm rate issue. In this study, a combined video analysis method (Convolutional Neural Network, CNN) and temporal feature analysis model (Long Short-Term Memory, LSTM) was proposed. To be specific, we used CNN-LSTM and Convolutional Neural Networks and Long Short-Term Memory (Resnet-LSTM) to perform the classifications for high risk scenarios and non-conflict scenarios. The empirical analyses have been conducted using commercial vehicle FOT data. And the results showed that the overall model performance (AUC index) in the test set could reach 0.91 with 83% accuracy rate. Finally, the future works have been discussed from the aspects of further extractions of video data and investigations of LSTM modelling results.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121893638","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294539
Maximilian Zipfl, Tobias Fleck, M. Zofka, Johann Marius Zöllner
The validation and verification (V&V) of highly automated driving depends on reference data in the different steps of the development process. While there exist a variety of datasets to optimize and benchmark perception algorithms, large trajectory datasets with complex interactions between traffic participants are quite rare. Such data offers the opportunity to derive virtual traffic scenarios and road user behaviour models that can be used to evaluate in-vehicle perception systems as well as decision making modules.We present the Test Area Autonomous Driving BadenWürttemberg (TAF-BW) Dataset that provides globally referenced road user trajectories extended with context information like traffic light signals and high definition map data recorded in the test area. We point out an exemplary application that becomes possible with the dataset: building semantic scene models that can be used for criticality assessment in traffic scenes. We conclude with a short outlook on future extensions.
{"title":"From Traffic Sensor Data To Semantic Traffic Descriptions: The Test Area Autonomous Driving Baden-Württemberg Dataset (TAF-BW Dataset)","authors":"Maximilian Zipfl, Tobias Fleck, M. Zofka, Johann Marius Zöllner","doi":"10.1109/ITSC45102.2020.9294539","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294539","url":null,"abstract":"The validation and verification (V&V) of highly automated driving depends on reference data in the different steps of the development process. While there exist a variety of datasets to optimize and benchmark perception algorithms, large trajectory datasets with complex interactions between traffic participants are quite rare. Such data offers the opportunity to derive virtual traffic scenarios and road user behaviour models that can be used to evaluate in-vehicle perception systems as well as decision making modules.We present the Test Area Autonomous Driving BadenWürttemberg (TAF-BW) Dataset that provides globally referenced road user trajectories extended with context information like traffic light signals and high definition map data recorded in the test area. We point out an exemplary application that becomes possible with the dataset: building semantic scene models that can be used for criticality assessment in traffic scenes. We conclude with a short outlook on future extensions.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121933032","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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