Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856466
Xiao Hu, S. R. Florez, A. Gepperth
Reliable road detection is a key issue for modern Intelligent Vehicles, since it can help to identify the driv-able area as well as boosting other perception functions like object detection. However, real environments present several challenges like illumination changes and varying weather conditions. We propose a multi-modal road detection and segmentation method based on monocular images and HD multi-layer LIDAR data (3D point cloud). This algorithm consists of three stages: extraction of ground points from multilayer LIDAR, transformation of color camera information to an illumination-invariant representation, and lastly the segmentation of the road area. For the first module, the core function is to extract the ground points from LIDAR data. To this end a road boundary detection is performed based on histogram analysis, then a plane estimation using RANSAC, and a ground point extraction according to the point-to-plane distance. In the second module, an image representation of illumination-invariant features is computed simultaneously. Ground points are projected to image plane and then used to compute a road probability map using a Gaussian model. The combination of these modalities improves the robustness of the whole system and reduces the overall computational time, since the first two modules can be run in parallel. Quantitative experiments carried on the public KITTI dataset enhanced by road annotations confirmed the effectiveness of the proposed method.
{"title":"A multi-modal system for road detection and segmentation","authors":"Xiao Hu, S. R. Florez, A. Gepperth","doi":"10.1109/IVS.2014.6856466","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856466","url":null,"abstract":"Reliable road detection is a key issue for modern Intelligent Vehicles, since it can help to identify the driv-able area as well as boosting other perception functions like object detection. However, real environments present several challenges like illumination changes and varying weather conditions. We propose a multi-modal road detection and segmentation method based on monocular images and HD multi-layer LIDAR data (3D point cloud). This algorithm consists of three stages: extraction of ground points from multilayer LIDAR, transformation of color camera information to an illumination-invariant representation, and lastly the segmentation of the road area. For the first module, the core function is to extract the ground points from LIDAR data. To this end a road boundary detection is performed based on histogram analysis, then a plane estimation using RANSAC, and a ground point extraction according to the point-to-plane distance. In the second module, an image representation of illumination-invariant features is computed simultaneously. Ground points are projected to image plane and then used to compute a road probability map using a Gaussian model. The combination of these modalities improves the robustness of the whole system and reduces the overall computational time, since the first two modules can be run in parallel. Quantitative experiments carried on the public KITTI dataset enhanced by road annotations confirmed the effectiveness of the proposed method.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124276783","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856551
Johannes Beck, C. Stiller
Lane estimation of the ego vehicle plays a key role in navigating a car through unknown areas. In fact, solving this problem is a prerequisite for any vehicle driving autonomously in previously unmapped areas. Most of the proposed methods for lane detection are tuned for freeways and rural environments. In urban scenarios, however, they are unable to reliably detect the ego lane in many situations. Often, these methods simply work on the principle of fitting a parametric model to lane markers. Since a large variety of lane shapes are found in urban environments, it is obvious that these models are too restrictive. Moreover, the complex structure of intersection-like situations further hampers the success of the aforementioned methods. Therefore we propose a non-parametric lane model which can handle a wide range of different features such as grass verge, free space, lane markers etc. The ego lane estimation is formulated as a shortest path problem. A directed acyclic graph is constructed from the feature pool rendering it efficiently solvable. The proposed approach is easily extendable as it is able to cope with pixel-wise low level features as well as highlevel ones jointly. We demonstrate the potential of our method in urban and rural areas and present experimental findings on difficult real world data sets.
{"title":"Non-parametric lane estimation in urban environments","authors":"Johannes Beck, C. Stiller","doi":"10.1109/IVS.2014.6856551","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856551","url":null,"abstract":"Lane estimation of the ego vehicle plays a key role in navigating a car through unknown areas. In fact, solving this problem is a prerequisite for any vehicle driving autonomously in previously unmapped areas. Most of the proposed methods for lane detection are tuned for freeways and rural environments. In urban scenarios, however, they are unable to reliably detect the ego lane in many situations. Often, these methods simply work on the principle of fitting a parametric model to lane markers. Since a large variety of lane shapes are found in urban environments, it is obvious that these models are too restrictive. Moreover, the complex structure of intersection-like situations further hampers the success of the aforementioned methods. Therefore we propose a non-parametric lane model which can handle a wide range of different features such as grass verge, free space, lane markers etc. The ego lane estimation is formulated as a shortest path problem. A directed acyclic graph is constructed from the feature pool rendering it efficiently solvable. The proposed approach is easily extendable as it is able to cope with pixel-wise low level features as well as highlevel ones jointly. We demonstrate the potential of our method in urban and rural areas and present experimental findings on difficult real world data sets.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125880073","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856605
Aayush Bansal, H. Badino, Daniel F. Huber
Localization is a central problem for intelligent vehicles. Visual localization can supplement or replace GPS-based localization approaches in situations where GPS is unavailable or inaccurate. Although visual localization has been demonstrated in a variety of algorithms and systems, the problem of how to best configure such a system remains largely an open question. Design choices, such as “where should the camera be placed?” and “how should it be oriented?” can have substantial effect on the cost and robustness of a fielded intelligent vehicle. This paper analyzes how different sensor configuration parameters and environmental conditions affect visual localization performance with the goal of understanding what causes certain configurations to perform better than others and providing general principles for configuring systems for visual localization. We ground the investigation using extensive field testing of a visual localization algorithm, and the data sets used for the analysis are made available for comparative evaluation.
{"title":"Understanding how camera configuration and environmental conditions affect appearance-based localization","authors":"Aayush Bansal, H. Badino, Daniel F. Huber","doi":"10.1109/IVS.2014.6856605","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856605","url":null,"abstract":"Localization is a central problem for intelligent vehicles. Visual localization can supplement or replace GPS-based localization approaches in situations where GPS is unavailable or inaccurate. Although visual localization has been demonstrated in a variety of algorithms and systems, the problem of how to best configure such a system remains largely an open question. Design choices, such as “where should the camera be placed?” and “how should it be oriented?” can have substantial effect on the cost and robustness of a fielded intelligent vehicle. This paper analyzes how different sensor configuration parameters and environmental conditions affect visual localization performance with the goal of understanding what causes certain configurations to perform better than others and providing general principles for configuring systems for visual localization. We ground the investigation using extensive field testing of a visual localization algorithm, and the data sets used for the analysis are made available for comparative evaluation.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130282092","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856615
C. Olaverri-Monreal, Joel Gonçalves, K. Bengler
Traveling with children in tow can pose a serious distraction to the driver, effectively drawing much of the necessary attention away from the road and causing a disruption in normal driving patterns. In this paper we investigate the driver's capacity to operate a vehicle safely when being exposed to a noise stimulus, specifically in the form of a crying baby for an extended period of time. For this purpose, we developed a tailored driving simulator framework to efficiently configure new experiments, built on modular components to make it easier to upgrade and update the experiment scenario and overall conditions. We then compared the driving behavior of parents to individuals without children focusing particularly on the affects on driving performance when a sudden event occurred on the road. We aim to study driving patterns under stressful conditions such as having children as occupants in the vehicle to be able to classify drivers for background training purposes regarding in-vehicle behavior. Results have shown the tendencies of parents when having a baby in the vehicle to produce better driving performances.
{"title":"Studying the driving performance of drivers with children aboard by means of a framework for flexible experiment configuration","authors":"C. Olaverri-Monreal, Joel Gonçalves, K. Bengler","doi":"10.1109/IVS.2014.6856615","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856615","url":null,"abstract":"Traveling with children in tow can pose a serious distraction to the driver, effectively drawing much of the necessary attention away from the road and causing a disruption in normal driving patterns. In this paper we investigate the driver's capacity to operate a vehicle safely when being exposed to a noise stimulus, specifically in the form of a crying baby for an extended period of time. For this purpose, we developed a tailored driving simulator framework to efficiently configure new experiments, built on modular components to make it easier to upgrade and update the experiment scenario and overall conditions. We then compared the driving behavior of parents to individuals without children focusing particularly on the affects on driving performance when a sudden event occurred on the road. We aim to study driving patterns under stressful conditions such as having children as occupants in the vehicle to be able to classify drivers for background training purposes regarding in-vehicle behavior. Results have shown the tendencies of parents when having a baby in the vehicle to produce better driving performances.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130494024","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856593
M. Zofka, R. Kohlhaas, T. Schamm, Johann Marius Zöllner
The design and development process of advanced driver assistance systems (ADAS) is divided into different phases, where the algorithms are implemented as a model, then as software and finally as hardware. Since it is unfeasable to simulate all possible driving situations for environmental perception and interpretation algorithms, there is still a need for expensive and time-consuming real test drives of thousands of kilometers. Therefore we present a novel approach for testing and evaluation of vision-based ADAS, where reliable simulations are fused with recorded data from test drives to provide a task-specific reference model. This approach provides ground truth with much higher reliability and reproducability than real test drives and authenticity than using pure simulations and can be applied already in early steps of the design process. We illustrate the effectiveness of our approach by testing a vision-based collision mitigation system on recordings of a german highway.
{"title":"Semivirtual simulations for the evaluation of vision-based ADAS","authors":"M. Zofka, R. Kohlhaas, T. Schamm, Johann Marius Zöllner","doi":"10.1109/IVS.2014.6856593","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856593","url":null,"abstract":"The design and development process of advanced driver assistance systems (ADAS) is divided into different phases, where the algorithms are implemented as a model, then as software and finally as hardware. Since it is unfeasable to simulate all possible driving situations for environmental perception and interpretation algorithms, there is still a need for expensive and time-consuming real test drives of thousands of kilometers. Therefore we present a novel approach for testing and evaluation of vision-based ADAS, where reliable simulations are fused with recorded data from test drives to provide a task-specific reference model. This approach provides ground truth with much higher reliability and reproducability than real test drives and authenticity than using pure simulations and can be applied already in early steps of the design process. We illustrate the effectiveness of our approach by testing a vision-based collision mitigation system on recordings of a german highway.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130949176","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856399
S. Silberstein, Dan Levi, V. Kogan, R. Gazit
We present a new vision-based pedestrian detection system for rear-view cameras which is robust to partial occlusions and non-upright poses. Detection is made using a single automotive rear-view fisheye lens camera. The system uses “Accelerated Feature Synthesis”, a multiple-part based detection method with state-of-the-art performance. In addition, we collected and annotated an extensive dataset of videos for this specific application which includes pedestrians in a wide range of environmental conditions. Using this dataset we demonstrate the benefits of using part-based detection for detecting people in various poses and under occlusions. We also show, using a measure developed specifically for video-based evaluation, the gain in detection accuracy compared with template-based detection.
{"title":"Vision-based pedestrian detection for rear-view cameras","authors":"S. Silberstein, Dan Levi, V. Kogan, R. Gazit","doi":"10.1109/IVS.2014.6856399","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856399","url":null,"abstract":"We present a new vision-based pedestrian detection system for rear-view cameras which is robust to partial occlusions and non-upright poses. Detection is made using a single automotive rear-view fisheye lens camera. The system uses “Accelerated Feature Synthesis”, a multiple-part based detection method with state-of-the-art performance. In addition, we collected and annotated an extensive dataset of videos for this specific application which includes pedestrians in a wide range of environmental conditions. Using this dataset we demonstrate the benefits of using part-based detection for detecting people in various poses and under occlusions. We also show, using a measure developed specifically for video-based evaluation, the gain in detection accuracy compared with template-based detection.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131331716","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856607
Ashish Tawari, M. Trivedi
Analysis of driver's head behavior is an integral part of driver monitoring system. Driver's coarse gaze direction or gaze zone is a very important cue in understanding driver-state. Many existing gaze zone estimators are, however, limited to single camera perspectives, which are vulnerable to occlusions of facial features from spatially large head movements away from the frontal pose. Non-frontal glances away from the driving direction, though, are of special interest as interesting events, critical to driver safety, occur during those times. In this paper, we present a distributed camera framework for gaze zone estimation using head pose dynamics to operate robustly and continuously even during large head movements. For experimental evaluations, we collected a dataset from naturalistic on-road driving in urban streets and freeways. A human expert provided the gaze zone ground truth using all vision information including eyes and surround context. Our emphasis is to understand the efficacy of the head pose dynamic information in predicting eye-gaze-based zone ground truth. We conducted several experiments in designing the dynamic features and compared the performance against static head pose based approach. Analyses show that dynamic information significantly improves the results.
{"title":"Robust and continuous estimation of driver gaze zone by dynamic analysis of multiple face videos","authors":"Ashish Tawari, M. Trivedi","doi":"10.1109/IVS.2014.6856607","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856607","url":null,"abstract":"Analysis of driver's head behavior is an integral part of driver monitoring system. Driver's coarse gaze direction or gaze zone is a very important cue in understanding driver-state. Many existing gaze zone estimators are, however, limited to single camera perspectives, which are vulnerable to occlusions of facial features from spatially large head movements away from the frontal pose. Non-frontal glances away from the driving direction, though, are of special interest as interesting events, critical to driver safety, occur during those times. In this paper, we present a distributed camera framework for gaze zone estimation using head pose dynamics to operate robustly and continuously even during large head movements. For experimental evaluations, we collected a dataset from naturalistic on-road driving in urban streets and freeways. A human expert provided the gaze zone ground truth using all vision information including eyes and surround context. Our emphasis is to understand the efficacy of the head pose dynamic information in predicting eye-gaze-based zone ground truth. We conducted several experiments in designing the dynamic features and compared the performance against static head pose based approach. Analyses show that dynamic information significantly improves the results.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128866217","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856540
Kuo-Yun Liang, J. Mårtensson, K. Johansson
Vehicle platooning is important for heavy-duty vehicle manufacturers, due to the reduced aerodynamic drag for the follower vehicles, which gives an overall lower fuel consumption. Heavy-duty vehicle drivers are aware this fact and sometimes drive close to other heavy-duty vehicles. However, it is not currently well known how many vehicles are actually driving in such spontaneous platoons today. This paper studies the platooning rate of 1,800 heavy-duty vehicles by analyzing sparse vehicle position data from a region in Europe during one day. Map-matching and path-inference algorithms are used to determine which paths the vehicles took. The spontaneous platooning rate is found to be 1.2 %, which corresponds to a total fuel saving of 0.07% compared to if none of the vehicles were platooning. Furthermore, we introduce several virtual coordination schemes. We show that coordinations can increase the platooning rate and fuel saving with a factor of ten with minor adjustments from the current travel schedule. The platooning rate and fuel savings can be significantly greater if higher flexibility is allowed.
{"title":"Fuel-saving potentials of platooning evaluated through sparse heavy-duty vehicle position data","authors":"Kuo-Yun Liang, J. Mårtensson, K. Johansson","doi":"10.1109/IVS.2014.6856540","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856540","url":null,"abstract":"Vehicle platooning is important for heavy-duty vehicle manufacturers, due to the reduced aerodynamic drag for the follower vehicles, which gives an overall lower fuel consumption. Heavy-duty vehicle drivers are aware this fact and sometimes drive close to other heavy-duty vehicles. However, it is not currently well known how many vehicles are actually driving in such spontaneous platoons today. This paper studies the platooning rate of 1,800 heavy-duty vehicles by analyzing sparse vehicle position data from a region in Europe during one day. Map-matching and path-inference algorithms are used to determine which paths the vehicles took. The spontaneous platooning rate is found to be 1.2 %, which corresponds to a total fuel saving of 0.07% compared to if none of the vehicles were platooning. Furthermore, we introduce several virtual coordination schemes. We show that coordinations can increase the platooning rate and fuel saving with a factor of ten with minor adjustments from the current travel schedule. The platooning rate and fuel savings can be significantly greater if higher flexibility is allowed.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115957917","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856440
C. D'Agostino, A. Saidi, Gilles Scouarnec, Liming Chen
Truck drivers typically display different behaviors when facing various driving events, e.g., approaching a roundabout, and thereby have a major impact both on the fuel consumption and the vehicle speed. Within the context where fuel is increasingly a major cost center for merchandise transport companies, it is important to recognize different driver behaviors in order to be able to simulate them as closely to the real data as possible during the truck development process. In this paper, we introduce, instead of economic driving, the notion of rational driving which seeks to decrease the average fuel consumption while respecting the transport companies' constraint, i.e., the delivery delay. Moreover, we also propose an indicator, namely rational driving index (RDI), which enables to quantify how good a driver behavior is with respect to the rational driving. We then investigate various driving features contributing to characterize a rational driver behavior, using real driving data collected from 34 different truck drivers on an extra-urban road section particularly representative of travel paths of trucks ensuring regional merchandise distribution. Given the fact that real driving data collected on an open road can differ in terms of environment, e.g., weather, traffic, we further study, through simulations on a digital representation of a roundabout, the impact of two major driving features, i.e., the use of coasting and crossing speed at roundabouts, with respect to rational driving. The experimental results from both real driving data and simulations show high correlations of these two driving features with respect to RDI and demonstrate that a good rational driver tends to decelerate slowly during braking periods (use of coasting) and have high crossing speed in roundabouts.
{"title":"Rational truck driving and its correlated driving features in extra-urban areas","authors":"C. D'Agostino, A. Saidi, Gilles Scouarnec, Liming Chen","doi":"10.1109/IVS.2014.6856440","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856440","url":null,"abstract":"Truck drivers typically display different behaviors when facing various driving events, e.g., approaching a roundabout, and thereby have a major impact both on the fuel consumption and the vehicle speed. Within the context where fuel is increasingly a major cost center for merchandise transport companies, it is important to recognize different driver behaviors in order to be able to simulate them as closely to the real data as possible during the truck development process. In this paper, we introduce, instead of economic driving, the notion of rational driving which seeks to decrease the average fuel consumption while respecting the transport companies' constraint, i.e., the delivery delay. Moreover, we also propose an indicator, namely rational driving index (RDI), which enables to quantify how good a driver behavior is with respect to the rational driving. We then investigate various driving features contributing to characterize a rational driver behavior, using real driving data collected from 34 different truck drivers on an extra-urban road section particularly representative of travel paths of trucks ensuring regional merchandise distribution. Given the fact that real driving data collected on an open road can differ in terms of environment, e.g., weather, traffic, we further study, through simulations on a digital representation of a roundabout, the impact of two major driving features, i.e., the use of coasting and crossing speed at roundabouts, with respect to rational driving. The experimental results from both real driving data and simulations show high correlations of these two driving features with respect to RDI and demonstrate that a good rational driver tends to decelerate slowly during braking periods (use of coasting) and have high crossing speed in roundabouts.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127040793","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856500
Rhea Valentina, A. Viehl, O. Bringmann, W. Rosenstiel
The HVAC system is considered as the largest auxiliary power load in electric vehicles (EV). Therefore, this paper presents a detailed modeling of an EV-based HVAC system to support a priori prediction of HVAC system energy consumption under consideration of the EV users thermal comfort. This prediction is integrated into a navigation system to allow the driver entering the preferred parameters of thermal comfort and advising the driver about the predicted overall energy consumption. The advice acceptance might increase the awareness of the driver regarding the potential saved energy and leads to an energy-efficient vehicle operation by extending the overall driving range.
{"title":"HVAC system modeling for range prediction of electric vehicles","authors":"Rhea Valentina, A. Viehl, O. Bringmann, W. Rosenstiel","doi":"10.1109/IVS.2014.6856500","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856500","url":null,"abstract":"The HVAC system is considered as the largest auxiliary power load in electric vehicles (EV). Therefore, this paper presents a detailed modeling of an EV-based HVAC system to support a priori prediction of HVAC system energy consumption under consideration of the EV users thermal comfort. This prediction is integrated into a navigation system to allow the driver entering the preferred parameters of thermal comfort and advising the driver about the predicted overall energy consumption. The advice acceptance might increase the awareness of the driver regarding the potential saved energy and leads to an energy-efficient vehicle operation by extending the overall driving range.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126849399","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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