Pub Date : 2015-11-01DOI: 10.1109/ICVES.2015.7396926
Jun Sun, Chunxiao Li, Jie Ding, Jing Yang, Zhi Liu
Unsuitable signalized intersection traffic lights control strategies lead to longer waiting time before vehicles driving through the signal intersections, and the long waiting time brings us many issues, such as traffic jams, more CO2 emissions. Hence how to reduce the waiting time becomes critical. In this paper, we propose a Markov chain based traffic flow evacuation model that aims at minimizing vehicles' CO2 emissions. Here suppose we are able to obtain the steady state probability p1 of vehicles arriving at the signalized intersection and the probability pcross that vehicles can drive through the signalized intersection. Then we propose a vehicles' speed control model, it's obtained with favourable performance about vehicles' CO2 emissions through these. Simulations are conducted to verify the performances of the proposed scheme and superior performance could be observed comparing with the competing scheme in typical network scenarios.
{"title":"A markov chain based traffic flow control model for reducing vehicles' CO2 emissions","authors":"Jun Sun, Chunxiao Li, Jie Ding, Jing Yang, Zhi Liu","doi":"10.1109/ICVES.2015.7396926","DOIUrl":"https://doi.org/10.1109/ICVES.2015.7396926","url":null,"abstract":"Unsuitable signalized intersection traffic lights control strategies lead to longer waiting time before vehicles driving through the signal intersections, and the long waiting time brings us many issues, such as traffic jams, more CO2 emissions. Hence how to reduce the waiting time becomes critical. In this paper, we propose a Markov chain based traffic flow evacuation model that aims at minimizing vehicles' CO2 emissions. Here suppose we are able to obtain the steady state probability p1 of vehicles arriving at the signalized intersection and the probability pcross that vehicles can drive through the signalized intersection. Then we propose a vehicles' speed control model, it's obtained with favourable performance about vehicles' CO2 emissions through these. Simulations are conducted to verify the performances of the proposed scheme and superior performance could be observed comparing with the competing scheme in typical network scenarios.","PeriodicalId":325462,"journal":{"name":"2015 IEEE International Conference on Vehicular Electronics and Safety (ICVES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122227576","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 : 2015-11-01DOI: 10.1109/ICVES.2015.7396885
Oussama Derbel, R. Landry
This paper proposes to evaluate the driver safety by integrating the information of the Driver, the Vehicle and the Environment (DVE). The adopted strategy is composed of two fusion levels (local and global fusion levels) and use the Demptster-Shafer Theory (DST). The development of the Basic Probability Assignment (BPA) is based on the developed Fuzzy Inference System (FIS) (in the vehicle entity) and models. To reduce the complexity of the fusion problem in the Vehicle entity, the output membership function is fixed to the two developed FISs and the rule table is tuned relatively to this assumption. Application to the Canadian drivers in the city of Montreal shows the validity of the developed risk modes in case of one sample data and a driving mission. Comparison results between the Dempster-Shafer (DS) and the sixth version of the Proportional Conflict Redistribution (PCR6) combination rules show that the relevance of the PCR6 in case of high conflict between sources. Data related to the experimental test is recorded using the developed on-board data collector device that integrates an Inertial Navigation System (INS) and a Global Positioning System (GPS). Our approach assumes that the driving behavior is evaluated over the defined referential subsets which are the Low Risk (LR), Medium Risk (MR) and High Risk (HR), LE u MR and HR and the driving scores are the masses over these subsets.
本文提出了将驾驶员、车辆和环境信息(DVE)相结合的驾驶员安全评价方法。所采用的策略由两个融合层(局部融合层和全局融合层)组成，并使用dempster - shafer理论(DST)。基本概率分配(BPA)的发展是基于已开发的模糊推理系统(FIS)(在车辆实体)和模型。为了降低Vehicle实体中融合问题的复杂性，将输出隶属函数固定在两个已开发的FISs上，并将规则表相对于该假设进行调整。对加拿大蒙特利尔市驾驶员的应用表明，在一个样本数据和一次驾驶任务的情况下，所开发的风险模型是有效的。Dempster-Shafer (DS)和第六版比例冲突再分配(PCR6)组合规则的比较结果表明，PCR6在源间高冲突情况下具有相关性。与实验测试相关的数据使用开发的机载数据采集器设备进行记录，该设备集成了惯性导航系统(INS)和全球定位系统(GPS)。我们的方法假设驾驶行为在定义的参考子集上进行评估，这些子集是低风险(LR)，中风险(MR)和高风险(HR)， LE u MR和HR，驾驶分数是这些子集上的质量。
{"title":"Driver behavior assessment based on the belief theory in the driver-vehicle-environment system","authors":"Oussama Derbel, R. Landry","doi":"10.1109/ICVES.2015.7396885","DOIUrl":"https://doi.org/10.1109/ICVES.2015.7396885","url":null,"abstract":"This paper proposes to evaluate the driver safety by integrating the information of the Driver, the Vehicle and the Environment (DVE). The adopted strategy is composed of two fusion levels (local and global fusion levels) and use the Demptster-Shafer Theory (DST). The development of the Basic Probability Assignment (BPA) is based on the developed Fuzzy Inference System (FIS) (in the vehicle entity) and models. To reduce the complexity of the fusion problem in the Vehicle entity, the output membership function is fixed to the two developed FISs and the rule table is tuned relatively to this assumption. Application to the Canadian drivers in the city of Montreal shows the validity of the developed risk modes in case of one sample data and a driving mission. Comparison results between the Dempster-Shafer (DS) and the sixth version of the Proportional Conflict Redistribution (PCR6) combination rules show that the relevance of the PCR6 in case of high conflict between sources. Data related to the experimental test is recorded using the developed on-board data collector device that integrates an Inertial Navigation System (INS) and a Global Positioning System (GPS). Our approach assumes that the driving behavior is evaluated over the defined referential subsets which are the Low Risk (LR), Medium Risk (MR) and High Risk (HR), LE u MR and HR and the driving scores are the masses over these subsets.","PeriodicalId":325462,"journal":{"name":"2015 IEEE International Conference on Vehicular Electronics and Safety (ICVES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130187876","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 : 2015-11-01DOI: 10.1109/ICVES.2015.7396895
Yu Wen, Xuetao Zhang, Fei Wang, Jinsong Han
Accurately predicting drivers intent in advance could help ADAS reduce false alarm rate and improve performance. In this paper, we propose a driver intent prediction approach base on Hidden Conditional Random Fields model. The work can substantially utilize multiple dynamic characteristics of the driving signals, such as the steering wheel angle, lateral position, and drivers gaze compared with other batch process algorithm like Support Vector Machine (SVM). Moreover, it is more discriminative than traditional methods based on Hidden Markov Model (HMM). The experiments were carried out in a driving simulator, and we designed a more complex driving environment compared with previous works. In our experiment, the curvature of the road was not constant and the subjects could make lane change decision on their own. The results show that the proposed method outperforms over SVM and HMM. The prediction accuracy is 99% in 0.5s before the lane change, and 85% in 2s before the maneuver.
{"title":"Predicting driver lane change intent using HCRF","authors":"Yu Wen, Xuetao Zhang, Fei Wang, Jinsong Han","doi":"10.1109/ICVES.2015.7396895","DOIUrl":"https://doi.org/10.1109/ICVES.2015.7396895","url":null,"abstract":"Accurately predicting drivers intent in advance could help ADAS reduce false alarm rate and improve performance. In this paper, we propose a driver intent prediction approach base on Hidden Conditional Random Fields model. The work can substantially utilize multiple dynamic characteristics of the driving signals, such as the steering wheel angle, lateral position, and drivers gaze compared with other batch process algorithm like Support Vector Machine (SVM). Moreover, it is more discriminative than traditional methods based on Hidden Markov Model (HMM). The experiments were carried out in a driving simulator, and we designed a more complex driving environment compared with previous works. In our experiment, the curvature of the road was not constant and the subjects could make lane change decision on their own. The results show that the proposed method outperforms over SVM and HMM. The prediction accuracy is 99% in 0.5s before the lane change, and 85% in 2s before the maneuver.","PeriodicalId":325462,"journal":{"name":"2015 IEEE International Conference on Vehicular Electronics and Safety (ICVES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132519412","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 : 2015-11-01DOI: 10.1109/ICVES.2015.7396910
T. Hiraoka, Masafumi Hayakawa
A previous study proposed a driver-assistance system (DAS) using a haptic interface to encourage drivers to prepare for spontaneous deceleration behavior against potential collision risk. Driving simulator experiments showed that drivers' reaction time were shortened while using the haptic DAS. However, there existed concerns regarding drivers' risk compensation behavior while using the system. Therefore, the present paper proposes an improved version of the aforementioned system. Experiments were performed to better understand the perception characteristics of seat protrusion haptic stimulus, and furthermore, new features such as a safe driving evaluation feedback were added in order to prevent drivers' risk compensation behavior. Results of driving simulator experiments indicated promising effects of the improved system in comparison to the previous system.
{"title":"Haptic interface of driver-assistance system based on safe driving evaluation","authors":"T. Hiraoka, Masafumi Hayakawa","doi":"10.1109/ICVES.2015.7396910","DOIUrl":"https://doi.org/10.1109/ICVES.2015.7396910","url":null,"abstract":"A previous study proposed a driver-assistance system (DAS) using a haptic interface to encourage drivers to prepare for spontaneous deceleration behavior against potential collision risk. Driving simulator experiments showed that drivers' reaction time were shortened while using the haptic DAS. However, there existed concerns regarding drivers' risk compensation behavior while using the system. Therefore, the present paper proposes an improved version of the aforementioned system. Experiments were performed to better understand the perception characteristics of seat protrusion haptic stimulus, and furthermore, new features such as a safe driving evaluation feedback were added in order to prevent drivers' risk compensation behavior. Results of driving simulator experiments indicated promising effects of the improved system in comparison to the previous system.","PeriodicalId":325462,"journal":{"name":"2015 IEEE International Conference on Vehicular Electronics and Safety (ICVES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134579896","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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