2019 IEEE Intelligent Transportation Systems Conference (ITSC)最新文献

{"title":"Development of a Co-Simulation Framework for Systematic Generation of Scenarios for Testing and Validation of Automated Driving Systems*","authors":"Demin Nalic, A. Eichberger, Georg Hanzl, M. Fellendorf, Branko Rogic","doi":"10.1109/ITSC.2019.8916839","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8916839","url":null,"abstract":"Due to the sheer infinite number of test scenarios for test and validation of automated driving, stand-alone on-road testing of these systems is not reasonable, calling for the development of X-in-the-loop test methods. Recent advances in simulation methods are often based on simulation techniques where test scenarios are built considering stochastic traffic or deterministic predefined manoeuvres. To ensure realism, numerical robustness and usability of the test scenarios for both approaches, increasing effort must be invested in modelling the driving environment as well as vehicle and traffic dynamics. Especially traffic models are rarely realistically modelled in most current scenario generation and testing techniques. The goal of the present paper is to introduce a co-simulation framework for automated scenario generation with calibrated traffic flow models using measured data from an official test road in Austria and modelled in PTV Vissim. Combined with the Multi-Body-System vehicle development software IPG CarMaker, the presented co-simulation framework provides an approach for generation of realistic scenarios. This approach is demonstrated for a Highway Chauffeur function and allows future systematic testing.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"53 8 1","pages":"1895-1901"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91125088","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}

{"title":"Modeling of Incident-Induced Capacity Loss for Hurricane Evacuation Simulation","authors":"Yuan Zhu, K. Ozbay, Kun Xie, Hong Yang","doi":"10.1109/ITSC.2019.8917250","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917250","url":null,"abstract":"Modeling and simulation of hurricane evacuation is an important task in emergency planning and management. One typically ignored factor that affects the development of a reliable evacuation model is the uncertainty caused by the incident-induced capacity loss. Lately, the impact of incidents on evacuation has drawn increasingly attention among researchers and practitioners, but few of them thoroughly investigated it using the real data in the modeling and simulation context. This study aims to investigate the impact of various types of incidents on modeling and simulation of hurricane evacuation. Particularly, the incidents that occurred under actual hurricane conditions are examined and their impact on the capacity loss is modeled. The developed incident frequency and duration models are incorporated into the network assignment model to study traffic conditions under hurricane Sandy in New York. Results show that the consideration of incident-induced capacity loss can greatly change the outcome of the evacuation model. Our findings suggest the need to include a well calibrated and validated traffic incident generation module for modeling and simulating hurricane evacuation.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"1 1","pages":"613-618"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91358339","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}

{"title":"Modeling and Prediction of Human Behaviors based on Driving Data using Multi-Layer HMMs","authors":"Qi Deng, D. Söffker","doi":"10.1109/ITSC.2019.8917445","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917445","url":null,"abstract":"Understanding and predicting of human driving behavior play an important role in the development of Advanced Driver Assistance Systems (ADAS) for assisting drivers. In this contribution, a Multi-Layer (3-layer) Hidden Markov Models (HMM) approach is proposed and developed for predicting human driving behavior. For a single HMM, more inputs will cause a longer training time, higher complexity, and even overfitting. The proposed method can fit to complex situations, also when more inputs are considered. In this contribution the first layer is considered to predict driving behavior in certain single working cases, i.e. each input variable is used to train a single model independently in the first layer. The outputs are combined into different models containing different information in the second and third layers. All HMMs in combination with a prefilter are used to predict driving behavior in parallel. Lane changing, as a usual driving maneuver, will be used as representative example task to be predicted. Based on observations (training), the HMM algorithm calculates the most probable driving behavior through the observation sequences. Furthermore, the observed sequences are also used for training of HMM during modeling process.To define model parameters and to improve the model performance NSGA-II is used. Using experimental data taken from driving simulator, it can be concluded that selecting optimal parameters increase the performance of driving behavior prediction. The effectiveness of the suggested Multi-Layer HMMs has been successfully proved based on experiments. The results show that the newly introduced approach outperforms alternative approaches applied to the same data set.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"35 1","pages":"2014-2019"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82472735","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}

{"title":"Multi-lane Convoy Control for Autonomous Vehicles based on Distributed Graph and Potential Field","authors":"Li Gao, Duanfeng Chu, Yongxing Cao, Liping Lu, Chaozhong Wu","doi":"10.1109/ITSC.2019.8917409","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917409","url":null,"abstract":"Generally, multi-vehicle cooperative driving mainly refers to the vehicle platoon control in a single lane. However, due to the limits of queue length, communication distance and time delay, the traditional vehicle platoon may encounter string instability. This paper extends the traditional single-lane platoon and proposes a multi-lane convoy with better capacity and stability. Specifically, based on the distributed graph method, a formation strategy is proposed to improve its obstacle avoidance ability and stability. Moreover, the traffic field model is built by using the potential field approach to complete motion planning. Simulation has been carried out to show the performance of the proposed algorithm.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"36 1","pages":"2463-2469"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80324813","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}

{"title":"Adaptive Sensor Fault Detection and Isolation using Unscented Kalman Filter for Vehicle Positioning","authors":"Daiki Mori, H. Sugiura, Y. Hattori","doi":"10.1109/ITSC.2019.8917208","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917208","url":null,"abstract":"There is an increasing demand for sub-meter vehicle localization for advanced safety and autonomous systems. Fault detection and isolation (FDI) for sensor systems, such as camera, LIDAR, GNSS, and V2X has been a challenge because their performances are significantly affected by weather, geographical changes, and even spoofing. In this paper, a sensor FDI using Student’s t-distribution based adaptive unscented Kalman filter is presented. The proposed filter evaluate each sensor by Hotelling’s T2 test utilizing the predicted sensor output and its covariance. This method can assess the correlation between data that is generated within the same sensor, for accurate fault detection. In addition, measurement noise is adaptively updated by identifying both the covariance and the degree of freedom of the outlier robust Student’s t-distribution. The robustness and accuracy of the localization and measurement noise estimation is confirmed through simulation and an experiment on a highway scenario. Furthermore, the result also shows that the precise FDI can be achieved without any prior information regarding sensor measurement noise. The proposed algorithm enhances the reliability of future position based systems such as autonomous control or V2V safety brake.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"205 1","pages":"1298-1304"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80355145","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}

{"title":"Identification of Major Road Influence Area Using NDS Data","authors":"R. Thapa, S. Hallmark, Nicole Oneyear, O. Smadi","doi":"10.1109/ITSC.2019.8917428","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917428","url":null,"abstract":"Crashes at rural intersection make up almost one-third of rural crashes. Many studies have focused on the minor stream driver since they are typically the ones who initiate the sequence of events leading to a crash, such as failure to yield to traffic control. However, the actions of the mainline road driver can influence crash outcome and severity. For instance, an alert major street driver can take the necessary maneuvers to avoid a crash or lessen the severity.This study used NDS data to assess the number of major approach drivers who demonstrate a measureable response to an upcoming intersection. A binary model was developed to relate response point to roadway, driver, and environmental characteristics. The result from this study showed that about 32% of mainline drivers at the high speed rural minor street stop controlled intersections showed a measurable response. The majority of drivers responded 80 to 240 meters upstream of the intersection. The relationship between other characteristics and response was also modeled.Results can be used to indicate where drivers react to an upcoming minor street intersection which can inform sign and countermeasure placement. Additionally it demonstrates a method to which could be used to assess rural intersection countermeasures. For instance, a number of agencies in the US are utilizing intelligent transportation system countermeasures such as intersection collision warning systems. Understanding where drivers are likely to respond can help in placing these types of countermeasures.Results also have implications for connected and autonomous vehicles. If application developers understand how a mainline driver reacts to the presence of an intersection, it can guide warning systems for the minor approach vehicle. For instance, detecting a change in speed of the major approach driver could signify the mainline driver is aware of the minor street vehicle while lack of response could trigger an alert for the minor street driver. This is particularly helpful in assessing on-coming vehicle speed and gap selection are problematic for drivers at minor stop-controlled approach.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"52 1","pages":"4501-4505"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80373866","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}

{"title":"Cross Validation for CNN based Affordance Learning and Control for Autonomous Driving","authors":"Chen Sun, Lang Su, Sunsheng Gu, Jean M. Uwabeza Vianney, K. Qin, Dongpu Cao","doi":"10.1109/ITSC.2019.8917385","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917385","url":null,"abstract":"Autonomous driving has attracted a significant amount of research efforts over the last few decades owing to the exponential growth of computational power and reduced cost of sensors. As a safety-sensitive task, autonomous driving needs a detailed level of scene understanding of decision making, planning, and control. This paper investigates the Convolutional Neural Network (CNN) based methods for affordance learning in driving scene understanding. Various perception models are built and evaluated for driving scene affordance learning in both the virtual environment and real sampled data. We also propose a conditional control model that maps the extracted coarse set of driving affordances to low-level control condition on the given driving priors. The performance, merits of the CNN based perception models, and the control model are analyzed and cross-validated on both virtual and real data.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"15 1","pages":"1519-1524"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78167846","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}

{"title":"Interaction-Aware Approach for Online Parameter Estimation of a Multi-lane Intelligent Driver Model","authors":"J. Buyer, Dominic Waldenmayer, Nico Sußmann, R. Zöllner, Johann Marius Zöllner","doi":"10.1109/ITSC.2019.8917257","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917257","url":null,"abstract":"The paper presents a probabilistic approach for online parameter estimation of an enhanced car-following model appropriate for multi-lane traffic, which is based on an extension of the well-known Intelligent Driver Model (IDM). The approach explicitly considers the simultaneous influence of several interacting vehicles on the longitudinal dynamics of the ego-vehicle. Therefore, a method to extract the relevant reference vehicles considered in the proposed multi-lane car-following model is developed. In order to calibrate the model parameters online, a particle filter approach, which is able to deal with the overdetermined model structure, is employed. Experimental studies using a real highway scenario observed by vehicle surroundings sensors show the need of the online-calibrated multi-lane architecture. To use the model for vehicle speed prediction, a further learning-based extension is suggested which enables the adaption of the model parameters over the prediction horizon.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"6 1","pages":"3967-3973"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78687489","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}

{"title":"Real-time Parking Slot Detection for Camera-equipped Vehicles*","authors":"Timo Féret, Pramod Chandrashekhariah, N. Trujillo","doi":"10.1109/ITSC.2019.8917060","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917060","url":null,"abstract":"This paper proposes a novel camera-based approach for parking slot detection with markings, using surround-view cameras mounted on vehicles. By introducing a pipeline of marking-specific image feature extraction and novel filtering stages, we detect parking slot markings in pinhole cameras as well as in cameras with fisheye lenses without using a computationally intensive bird-view transformation. After projecting a compact set of image features into 3D space, our orientation-specific Hough transform finds explicitly the left and right edges of the parking slot markings in desired orientations, which is the basis for marking detection and tracking. We present a novel technique to detect and track the parking slots in the scene in a coherent manner, that preserves the structure of the overall parking layout and its temporal consistency. Our algorithm is designed to run on low cost hardware used in vehicles and it is shown to run at 30fps on ARM CPUs. We validated our algorithm on videos representing real-world scenarios of parking slots including marking occlusion, degradation and different weather conditions.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"43 1","pages":"4107-4114"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78786394","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}

{"title":"Multi-Objective Calibration of Microscopic Traffic Simulation for Highway Traffic Safety","authors":"Edgar Tamayo Cascan, Jordan Ivanchev, D. Eckhoff, A. Sangiovanni-Vincentelli, A. Knoll","doi":"10.1109/ITSC.2019.8917044","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917044","url":null,"abstract":"Microscopic traffic simulation has become an important tool to investigate traffic efficiency and road safety. In order to produce meaningful results, driver behaviour models need to be carefully calibrated to represent real world conditions. If this type of simulations are to be used to evaluate safety features of traffic, on top of macroscopic relationships such as the speed-density diagram, they should also adequately represent the average risk of accidents occurring on the road. In this paper, we present a two-stage computationally feasible multi-objective calibration process. The first stage performs a parameter sensitivity analysis to select only parameters with considerable effect on the respective objective functions. The second stage employs a multi-objective genetic algorithm utilizing only few influential parameters that produces a front of Pareto optimal solutions with respect to the conflicting objective functions. Compared to traditional methods which focus on only one objective while sacrificing the accuracy of the other, our method achieves a high degree of realism for both traffic flow and average risk.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"64 1","pages":"4548-4555"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76057713","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}