Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856424
S. Li, Shaobing Xu, Guofa Li, B. Cheng
Eco-driving technologies are able to largely reduce the fuel consumption of ground vehicles. This paper presents how to determine the fuel-optimized operating strategies of passenger cars under cruising process. The design naturally casts into an optimal control problem with the S-shaped engine fueling rate as the integrand of cost function. The solutions are numerically solved by the Legendre pseudospectral method, of which many are found to demonstrate periodic behaviors. In the periodic operation, the engine switches between the minimum brake specific fuel consumption (BSFC) point and the idling point, while the vehicle speed oscillates between its upper and lower bounds. The formation of periodic operation are analyzed and explained by the π-test theory and steady state analysis method.
{"title":"Periodicity based cruising control of passenger cars for optimized fuel consumption","authors":"S. Li, Shaobing Xu, Guofa Li, B. Cheng","doi":"10.1109/IVS.2014.6856424","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856424","url":null,"abstract":"Eco-driving technologies are able to largely reduce the fuel consumption of ground vehicles. This paper presents how to determine the fuel-optimized operating strategies of passenger cars under cruising process. The design naturally casts into an optimal control problem with the S-shaped engine fueling rate as the integrand of cost function. The solutions are numerically solved by the Legendre pseudospectral method, of which many are found to demonstrate periodic behaviors. In the periodic operation, the engine switches between the minimum brake specific fuel consumption (BSFC) point and the idling point, while the vehicle speed oscillates between its upper and lower bounds. The formation of periodic operation are analyzed and explained by the π-test theory and steady state analysis method.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"33 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":"122137078","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.2014.6856609
R. Satzoda, Pujitha Gunaratne, M. Trivedi
Naturalistic driving studies (NDS) provide critical information about driving behaviors and characteristics that could lead to crashes and near-crashes. Such studies involve analysis of large volumes of data from multiple sensors and detection and extraction of critical events is an important step in NDS. This paper introduces techniques that analyze the visual data complemented with other sensors in the vehicle to determine critical events related to lane drifts, road departures and road delineations. To the best knowledge of the authors, this is the first work that detects and extract events listed in visual reference dictionary of NDS studies like Strategic Highway Research Program 2 (SHRP2). Detailed evaluations with real-world NDS data is presented.
{"title":"Drive analysis using lane semantics for data reduction in naturalistic driving studies","authors":"R. Satzoda, Pujitha Gunaratne, M. Trivedi","doi":"10.1109/IVS.2014.6856609","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856609","url":null,"abstract":"Naturalistic driving studies (NDS) provide critical information about driving behaviors and characteristics that could lead to crashes and near-crashes. Such studies involve analysis of large volumes of data from multiple sensors and detection and extraction of critical events is an important step in NDS. This paper introduces techniques that analyze the visual data complemented with other sensors in the vehicle to determine critical events related to lane drifts, road departures and road delineations. To the best knowledge of the authors, this is the first work that detects and extract events listed in visual reference dictionary of NDS studies like Strategic Highway Research Program 2 (SHRP2). Detailed evaluations with real-world NDS data is presented.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"79 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":"129368369","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.2014.6856499
Stefan Koehler, A. Viehl, O. Bringmann, W. Rosenstiel
We propose a novel operation strategy for electric vehicles with axle-individual electric machines to improve their energy efficiency in typical driving situations. The developed algorithm is allocating a total torque requested by a velocity controlling system or the driver to the electric machines such that the energy loss is reduced compared to an equal distribution. By taking near-future forecasts into account, the predictive nature of the algorithm leads to a minimized number of clutching processes compared to previous work and thereby contributes to increased comfort and minimized component wear. Overall, an average reduction of up to 25% in the electric machine losses can be achieved for the ARTEMIS driving cycles. At the same time, a reduction of the clutching operations by 70% is possible due to the forecast, compared to algorithms only considering the momentary state.
{"title":"Energy-efficient torque distribution for axle-individually propelled electric vehicles","authors":"Stefan Koehler, A. Viehl, O. Bringmann, W. Rosenstiel","doi":"10.1109/IVS.2014.6856499","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856499","url":null,"abstract":"We propose a novel operation strategy for electric vehicles with axle-individual electric machines to improve their energy efficiency in typical driving situations. The developed algorithm is allocating a total torque requested by a velocity controlling system or the driver to the electric machines such that the energy loss is reduced compared to an equal distribution. By taking near-future forecasts into account, the predictive nature of the algorithm leads to a minimized number of clutching processes compared to previous work and thereby contributes to increased comfort and minimized component wear. Overall, an average reduction of up to 25% in the electric machine losses can be achieved for the ARTEMIS driving cycles. At the same time, a reduction of the clutching operations by 70% is possible due to the forecast, compared to algorithms only considering the momentary state.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"10 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":"125137240","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.2014.6856456
J. Brembeck, C. Winter
In this paper an energy optimal path planning and velocity profile generation for our highly maneuverable Robotic Electric Vehicle research platform ROboMObil is presented. The ROMO [1] is a development of the German Aerospace Center's Robotics and Mechatronics Center to cope with several research topics, like energy efficient, autonomous or remote controlled driving for future (electro-) mobility applications. The main task of the proposed algorithms is to calculate an energy optimal trajectory in a real-time capable way. It is designed to incorporate data from actual traffic situations (e.g. oncoming traffic) or changed conditions (e.g. snowy conditions). The resulting trajectory is then fed forward to a lower level time independent path following control [2] that calculates the motion demands for our energy optimal control allocation. This in turn distributes the demand to the actuators of the over-actuated vehicle. We show a numerical reliable way to formulate the energy optimal path planning optimization objective, which is able to provide a consistent replanning feature considering the actual vehicle states. Besides this, different types of optimization methods are evaluated for their real-time capabilities. The velocity profile will be calculated afterwards and the generation of the profile is also enabled to handle dynamic replanning. Finally, we show several experimental results, using a virtual road definition and tests on a commercial real-time platform.
{"title":"Real-time capable path planning for energy management systems in future vehicle architectures","authors":"J. Brembeck, C. Winter","doi":"10.1109/IVS.2014.6856456","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856456","url":null,"abstract":"In this paper an energy optimal path planning and velocity profile generation for our highly maneuverable Robotic Electric Vehicle research platform ROboMObil is presented. The ROMO [1] is a development of the German Aerospace Center's Robotics and Mechatronics Center to cope with several research topics, like energy efficient, autonomous or remote controlled driving for future (electro-) mobility applications. The main task of the proposed algorithms is to calculate an energy optimal trajectory in a real-time capable way. It is designed to incorporate data from actual traffic situations (e.g. oncoming traffic) or changed conditions (e.g. snowy conditions). The resulting trajectory is then fed forward to a lower level time independent path following control [2] that calculates the motion demands for our energy optimal control allocation. This in turn distributes the demand to the actuators of the over-actuated vehicle. We show a numerical reliable way to formulate the energy optimal path planning optimization objective, which is able to provide a consistent replanning feature considering the actual vehicle states. Besides this, different types of optimization methods are evaluated for their real-time capabilities. The velocity profile will be calculated afterwards and the generation of the profile is also enabled to handle dynamic replanning. Finally, we show several experimental results, using a virtual road definition and tests on a commercial real-time platform.","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":"130337646","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.2014.6856606
A. Brown, S. Brennan
Computer-based guidance of passenger vehicles is a common reality today, but cost, computation, and robustness challenges remain to obtain accurate vehicle state estimates. This study builds on previous work by the authors towards the development of a vehicle state estimation framework that uses optimal preview control theory to fuse map, GPS, inertial, and forward-looking camera information in a linear filter that offers a-priori predictions of state estimate accuracy. By designing an optimal preview controller around a preview filter designed to make full use of a test vehicle's low-cost sensors, on-board map, and available visibility, a matched perception and control system is obtained. The resulting preview-based guidance system has a structure similar to LQG algorithms, and is tested both in simulation and on a real vehicle. The closed loop system provides lane-level tracking performance with low cost sensors.
{"title":"Temporal preview estimation for design of a low cost lane-following system using a forward-facing monocular camera","authors":"A. Brown, S. Brennan","doi":"10.1109/IVS.2014.6856606","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856606","url":null,"abstract":"Computer-based guidance of passenger vehicles is a common reality today, but cost, computation, and robustness challenges remain to obtain accurate vehicle state estimates. This study builds on previous work by the authors towards the development of a vehicle state estimation framework that uses optimal preview control theory to fuse map, GPS, inertial, and forward-looking camera information in a linear filter that offers a-priori predictions of state estimate accuracy. By designing an optimal preview controller around a preview filter designed to make full use of a test vehicle's low-cost sensors, on-board map, and available visibility, a matched perception and control system is obtained. The resulting preview-based guidance system has a structure similar to LQG algorithms, and is tested both in simulation and on a real vehicle. The closed loop system provides lane-level tracking performance with low cost sensors.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"13 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":"123848075","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.2014.6856611
Quoc Huy Do, S. Mita, Keisuke Yoneda
This paper introduces a novel path planning method under non-holonomic constraint for car-like vehicles, which associates map discovery and heuristic search to attain an optimal resultant path. The map discovery applies fast marching method to investigate the map geometric information. After that, the support vector machine is performed to find obstacle clearance information. This information is then used as a heuristic function which helps greatly reduce the search space. The fast marching is performed again, guided by this function to generate vehicle motions under kinematic constraints. Experimental results have shown that this method is able to generate motions for non-holonomic vehicles. In comparison with related methods, the path generated by proposed method is smoother and stay farther away from the obstacles.
{"title":"Narrow passage path planning using fast marching method and support vector machine","authors":"Quoc Huy Do, S. Mita, Keisuke Yoneda","doi":"10.1109/IVS.2014.6856611","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856611","url":null,"abstract":"This paper introduces a novel path planning method under non-holonomic constraint for car-like vehicles, which associates map discovery and heuristic search to attain an optimal resultant path. The map discovery applies fast marching method to investigate the map geometric information. After that, the support vector machine is performed to find obstacle clearance information. This information is then used as a heuristic function which helps greatly reduce the search space. The fast marching is performed again, guided by this function to generate vehicle motions under kinematic constraints. Experimental results have shown that this method is able to generate motions for non-holonomic vehicles. In comparison with related methods, the path generated by proposed method is smoother and stay farther away from the obstacles.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"5 41","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120924065","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.2014.6856400
S. Tsugawa
This paper presents an automated truck platoon that has been developed within a national ITS project named "Energy ITS," and the results and future issues. The five-year project started in 2008 aimed at energy saving and global warming prevention with automated driving. A platoon of three fully-automated heavy trucks and also a fully-automated light truck drove at 80 km/h with the gap of up to 4.7 m on a test truck. The lateral control was based on the lane marker detection by computer vision, and the longitudinal control was based on gap measurement by 76 GHz radar and lidar in addition to the inter-vehicle communications of 5.8 GHz DSRC and infrared. The radar and lidar also worked as the obstacle detection. The feature of the technologies is high reliability. Fuel consumption measurement on a test track shows that the fuel can be saved by about 15 % when the gap was 4.7 m. A simulation study shows that the effectiveness of the platooning with the gap of 10 m when the 40 % penetration in heavy trucks is 2.1 % reduction of CO2 along an expressway. In addition, experiments of four heavy trucks with CACC were also conducted for the introduction scenario. The technological and non-technological issues on automated driving and its introduction are also discussed.
{"title":"Results and issues of an automated truck platoon within the energy ITS project","authors":"S. Tsugawa","doi":"10.1109/IVS.2014.6856400","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856400","url":null,"abstract":"This paper presents an automated truck platoon that has been developed within a national ITS project named \"Energy ITS,\" and the results and future issues. The five-year project started in 2008 aimed at energy saving and global warming prevention with automated driving. A platoon of three fully-automated heavy trucks and also a fully-automated light truck drove at 80 km/h with the gap of up to 4.7 m on a test truck. The lateral control was based on the lane marker detection by computer vision, and the longitudinal control was based on gap measurement by 76 GHz radar and lidar in addition to the inter-vehicle communications of 5.8 GHz DSRC and infrared. The radar and lidar also worked as the obstacle detection. The feature of the technologies is high reliability. Fuel consumption measurement on a test track shows that the fuel can be saved by about 15 % when the gap was 4.7 m. A simulation study shows that the effectiveness of the platooning with the gap of 10 m when the 40 % penetration in heavy trucks is 2.1 % reduction of CO2 along an expressway. In addition, experiments of four heavy trucks with CACC were also conducted for the introduction scenario. The technological and non-technological issues on automated driving and its introduction are also discussed.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"8 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":"124297402","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.2014.6856610
Sujitha Martin, Eshed Ohn-Bar, Ashish Tawari, M. Trivedi
In this work, we propose a vision-based analysis framework for recognizing in-vehicle activities such as interactions with the steering wheel, the instrument cluster and the gear. The framework leverages two views for activity analysis, a camera looking at the driver's hand and another looking at the driver's head. The techniques proposed can be used by researchers in order to extract `mid-level' information from video, which is information that represents some semantic understanding of the scene but may still require an expert in order to distinguish difficult cases or leverage the cues to perform drive analysis. Unlike such information, `low-level' video is large in quantity and can't be used unless processed entirely by an expert. This work can apply to minimizing manual labor so that researchers may better benefit from the accessibility of the data and provide them with the ability to perform larger-scaled studies.
{"title":"Understanding head and hand activities and coordination in naturalistic driving videos","authors":"Sujitha Martin, Eshed Ohn-Bar, Ashish Tawari, M. Trivedi","doi":"10.1109/IVS.2014.6856610","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856610","url":null,"abstract":"In this work, we propose a vision-based analysis framework for recognizing in-vehicle activities such as interactions with the steering wheel, the instrument cluster and the gear. The framework leverages two views for activity analysis, a camera looking at the driver's hand and another looking at the driver's head. The techniques proposed can be used by researchers in order to extract `mid-level' information from video, which is information that represents some semantic understanding of the scene but may still require an expert in order to distinguish difficult cases or leverage the cues to perform drive analysis. Unlike such information, `low-level' video is large in quantity and can't be used unless processed entirely by an expert. This work can apply to minimizing manual labor so that researchers may better benefit from the accessibility of the data and provide them with the ability to perform larger-scaled studies.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"6 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":"124392002","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.2014.6856513
A. Hata, Danilo Habermann, F. Osório, D. Wolf
An autonomous car must have a robust perception system to navigate safely in urban streets. An important issue of environment perception is the road (navigable area) detection and the identification of the road geometry. The road geometry information can be used to determine the vehicle control according to the street and also for topological localization. Existing road geometry identifiers only work with a limited number of classes and, due to the use of cameras, some solutions depend on filters to deal with shadows and light variations. This paper presents a road detector that extracts curb and navigable surface information from a multilayer laser sensor data. The road data was trained with an artificial neural network (ANN) and classified into eight road geometries: straight road, left turn, right turn, left side road, right side road, T intersection, Y intersection and crossroad. The main advantage of our method is its robustness to light variations for detecting distinct roads even in the presence of noisy data thanks to the ANN. In order to determine which road information has the best features for ANN training, three approaches were explored: ANN trained with curb data, ANN trained with surface data and ANN trained with both curb and surface data. Performed experiments resulted in the superiority of the network trained with both curb and surface data, with an accuracy of 0.91799. The trained ANN was validated in different urban scenarios and, evaluating a 1 Km track, we obtained a 94.48% of correct classifications. These results are superior than other works that detect fewer number of road shapes.
{"title":"Road geometry classification using ANN","authors":"A. Hata, Danilo Habermann, F. Osório, D. Wolf","doi":"10.1109/IVS.2014.6856513","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856513","url":null,"abstract":"An autonomous car must have a robust perception system to navigate safely in urban streets. An important issue of environment perception is the road (navigable area) detection and the identification of the road geometry. The road geometry information can be used to determine the vehicle control according to the street and also for topological localization. Existing road geometry identifiers only work with a limited number of classes and, due to the use of cameras, some solutions depend on filters to deal with shadows and light variations. This paper presents a road detector that extracts curb and navigable surface information from a multilayer laser sensor data. The road data was trained with an artificial neural network (ANN) and classified into eight road geometries: straight road, left turn, right turn, left side road, right side road, T intersection, Y intersection and crossroad. The main advantage of our method is its robustness to light variations for detecting distinct roads even in the presence of noisy data thanks to the ANN. In order to determine which road information has the best features for ANN training, three approaches were explored: ANN trained with curb data, ANN trained with surface data and ANN trained with both curb and surface data. Performed experiments resulted in the superiority of the network trained with both curb and surface data, with an accuracy of 0.91799. The trained ANN was validated in different urban scenarios and, evaluating a 1 Km track, we obtained a 94.48% of correct classifications. These results are superior than other works that detect fewer number of road shapes.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"36 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":"126528274","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.2014.6856435
Xuanpeng Li, E. Seignez, Wenjie Lu, P. Loonis
Vehicle safety is the study and practice for minimizing the occurrences and consequences of traffic accidents. It is found that driver behaviors such as drowsiness, impaired driving and distraction are contributing factors to traffic accidents. In complex road surroundings, comprehensive analysis is more robust than separate evaluations which are broadly proceeded with. In this paper, we propose a vision-based nonintrusive system involving lane and driver's eye features to analyze driver behaviors. In the framework of evidence theory, evaluations of driver drowsiness and distracted and impaired driving performance are integrated to evaluate vehicle safety in real time. The system was validated in real world scenarios, and experimental results demonstrate that it is promising to improve the robustness and temporal response of vehicle safety vigilance.
{"title":"Vehicle safety evaluation based on driver drowsiness and distracted and impaired driving performance using evidence theory","authors":"Xuanpeng Li, E. Seignez, Wenjie Lu, P. Loonis","doi":"10.1109/IVS.2014.6856435","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856435","url":null,"abstract":"Vehicle safety is the study and practice for minimizing the occurrences and consequences of traffic accidents. It is found that driver behaviors such as drowsiness, impaired driving and distraction are contributing factors to traffic accidents. In complex road surroundings, comprehensive analysis is more robust than separate evaluations which are broadly proceeded with. In this paper, we propose a vision-based nonintrusive system involving lane and driver's eye features to analyze driver behaviors. In the framework of evidence theory, evaluations of driver drowsiness and distracted and impaired driving performance are integrated to evaluate vehicle safety in real time. The system was validated in real world scenarios, and experimental results demonstrate that it is promising to improve the robustness and temporal response of vehicle safety vigilance.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"58 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113933422","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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