Pub Date : 2015-08-27DOI: 10.1109/IVS.2015.7225895
Youngil Koh, Hyundong Her, Kilsoo Kim, K. Yi
This paper describes an integrated driver model for vehicle-driver closed-loop simulation at high speed maneuvering. The proposed driver model is developed to specialize in limit handling, in order to be used as a validation platform of chassis control system. Thus, the proposed driver model emulates human driver's driving characteristics such as, desired path selection from varying preview area, deceleration against losing maneuverability. In high-speed cornering, steering with excessive corner-entry speed causes lateral tire force saturation readily. Sequentially, the lateral tire force saturation induces lateral instability of a vehicle. Deceleration is the most effective manipulation which driver can do. The proposed driver model is designed to utilize capability of tire force tightly, while securing lateral stability of the vehicle. The proposed driver model has been validated via comparison with an expert driver's driving data, collected on the Korea International Circuit in Yeongam, Korea.
{"title":"Driver model with motion stabilizer for vehicle-driver closed-loop simulation at high-speed maneuvering","authors":"Youngil Koh, Hyundong Her, Kilsoo Kim, K. Yi","doi":"10.1109/IVS.2015.7225895","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225895","url":null,"abstract":"This paper describes an integrated driver model for vehicle-driver closed-loop simulation at high speed maneuvering. The proposed driver model is developed to specialize in limit handling, in order to be used as a validation platform of chassis control system. Thus, the proposed driver model emulates human driver's driving characteristics such as, desired path selection from varying preview area, deceleration against losing maneuverability. In high-speed cornering, steering with excessive corner-entry speed causes lateral tire force saturation readily. Sequentially, the lateral tire force saturation induces lateral instability of a vehicle. Deceleration is the most effective manipulation which driver can do. The proposed driver model is designed to utilize capability of tire force tightly, while securing lateral stability of the vehicle. The proposed driver model has been validated via comparison with an expert driver's driving data, collected on the Korea International Circuit in Yeongam, Korea.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132357104","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225710
J. Wiest, Matthias Karg, Felix Kunz, Stephan Reuter, U. Kressel, K. Dietmayer
This contribution proposes a novel algorithm for predicting maneuvers at intersections. With applicability to driver assistance systems and autonomous driving, the presented methodology estimates a maneuver probability for every possible direction at an intersection. For this purpose, a generic intersection-feature, space-based representation is defined which combines static and dynamic intersection information with the dynamic properties of the observed vehicle, provided by a tracking module. A statistical behavior model is learned from previously recorded patterns by approximating the resulting feature space. Because the feature space consists of different types of features (mixed-feature space), a Bernoulli-Gaussian Mixture Model is applied as approximating function. Further, an online learning extension is proposed to adapt the model to the characteristics of different intersections.
{"title":"A probabilistic maneuver prediction framework for self-learning vehicles with application to intersections","authors":"J. Wiest, Matthias Karg, Felix Kunz, Stephan Reuter, U. Kressel, K. Dietmayer","doi":"10.1109/IVS.2015.7225710","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225710","url":null,"abstract":"This contribution proposes a novel algorithm for predicting maneuvers at intersections. With applicability to driver assistance systems and autonomous driving, the presented methodology estimates a maneuver probability for every possible direction at an intersection. For this purpose, a generic intersection-feature, space-based representation is defined which combines static and dynamic intersection information with the dynamic properties of the observed vehicle, provided by a tracking module. A statistical behavior model is learned from previously recorded patterns by approximating the resulting feature space. Because the feature space consists of different types of features (mixed-feature space), a Bernoulli-Gaussian Mixture Model is applied as approximating function. Further, an online learning extension is proposed to adapt the model to the characteristics of different intersections.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133974792","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225756
Andra Petrovai, R. Danescu, S. Nedevschi
This paper presents SmartCoDrive, an Android application which performs driving assistance functions: 3D lane detection and tracking, forward obstacle detection, obstacle tracking. With this mobile application we wish to increase the adoption rate of driving assistance systems and to provide a viable and cheap solution for every driver, that will be able to use his own tablet or smartphone as a personal driving assistant. The mobile application is deployed on a tablet equipped with dual back-facing cameras. The visual information from the two cameras, along with the data received from the Controller Area Network bus of the vehicle enable a thorough understanding of the 3D environment. First, we develop the sparse 3D reconstruction algorithm. Then, using monocular vision we perform lane markings detection. Obstacle detection is done by combining the superpixel segmentation with 3D information and the tracking algorithm is based on the Kalman Filter. Since the processing capabilities of the mobile platforms are limited, different optimizations are carried out in order to obtain a real-time implementation. The Android application may be used in urban traffic that is characterized by low-speed and short-medium distances to obstacles.
{"title":"A stereovision based approach for detecting and tracking lane and forward obstacles on mobile devices","authors":"Andra Petrovai, R. Danescu, S. Nedevschi","doi":"10.1109/IVS.2015.7225756","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225756","url":null,"abstract":"This paper presents SmartCoDrive, an Android application which performs driving assistance functions: 3D lane detection and tracking, forward obstacle detection, obstacle tracking. With this mobile application we wish to increase the adoption rate of driving assistance systems and to provide a viable and cheap solution for every driver, that will be able to use his own tablet or smartphone as a personal driving assistant. The mobile application is deployed on a tablet equipped with dual back-facing cameras. The visual information from the two cameras, along with the data received from the Controller Area Network bus of the vehicle enable a thorough understanding of the 3D environment. First, we develop the sparse 3D reconstruction algorithm. Then, using monocular vision we perform lane markings detection. Obstacle detection is done by combining the superpixel segmentation with 3D information and the tracking algorithm is based on the Kalman Filter. Since the processing capabilities of the mobile platforms are limited, different optimizations are carried out in order to obtain a real-time implementation. The Android application may be used in urban traffic that is characterized by low-speed and short-medium distances to obstacles.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116142435","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225668
JeongMok Ha, Byeongchan Jeon, WooYeol Jun, Joonho Lee, Hong Jeong
In advanced driver assistance systems, the stereo matching algorithm is the key resource to obtain depth information of outdoor scenes. Semi-Global Matching (SGM) is currently the most efficient stereo matching algorithm for outdoor environments. However, because the number of pixels is large, SGM uses only a subset of them when estimating the disparity of a pixel. To overcome this limitation, Cost Aggregation Table (CAT) was proposed which uses two-dimensional cost aggregation so as to utilize whole image information. In this paper, we propose improved global 2D cost aggregation methods by loosening aggregation constraints. It aggregates every cost in the whole image to estimate each disparity. Although our method aggregates every cost in the image, the computational complexity is the same as that of SGM and CAT. The proposed cost aggregation method achieves superior disparity accuracy compared to the SGM.
{"title":"An improved 2D cost aggregation method for advanced driver assistance systems","authors":"JeongMok Ha, Byeongchan Jeon, WooYeol Jun, Joonho Lee, Hong Jeong","doi":"10.1109/IVS.2015.7225668","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225668","url":null,"abstract":"In advanced driver assistance systems, the stereo matching algorithm is the key resource to obtain depth information of outdoor scenes. Semi-Global Matching (SGM) is currently the most efficient stereo matching algorithm for outdoor environments. However, because the number of pixels is large, SGM uses only a subset of them when estimating the disparity of a pixel. To overcome this limitation, Cost Aggregation Table (CAT) was proposed which uses two-dimensional cost aggregation so as to utilize whole image information. In this paper, we propose improved global 2D cost aggregation methods by loosening aggregation constraints. It aggregates every cost in the whole image to estimate each disparity. Although our method aggregates every cost in the image, the computational complexity is the same as that of SGM and CAT. The proposed cost aggregation method achieves superior disparity accuracy compared to the SGM.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116149726","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225855
Mohammad Shokrolah Shirazi, B. Morris
This article presents a review of recent literature of intersection behavior analysis for three types of intersection participants; vehicles, drivers, and pedestrians. In this survey, behavior analysis of each participant group is discussed based on key features and elements used for intersection design, planning and safety analysis. Different methods used for data collection, behavior recognition and analysis are reviewed for each group and a discussion is provided on the state of the art along with challenges and future research directions in the field.
{"title":"Observing behaviors at intersections: A review of recent studies & developments","authors":"Mohammad Shokrolah Shirazi, B. Morris","doi":"10.1109/IVS.2015.7225855","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225855","url":null,"abstract":"This article presents a review of recent literature of intersection behavior analysis for three types of intersection participants; vehicles, drivers, and pedestrians. In this survey, behavior analysis of each participant group is discussed based on key features and elements used for intersection design, planning and safety analysis. Different methods used for data collection, behavior recognition and analysis are reviewed for each group and a discussion is provided on the state of the art along with challenges and future research directions in the field.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129392850","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225672
Vishakh Duggal, K. Bipin, Ashutosh Kumar Singh, Bharath Gopalakrishnan, B. Bharti, A. Khiat, K. Krishna
Overtaking of a vehicle moving on structured roads is one of the most frequent driving behavior. In this work, we have described a Real Time Control System based framework for overtaking maneuver of autonomous vehicles. Proposed framework incorporates Intelligent Planning and Modular control modules. Intelligent Planning module of the framework enables the vehicle to intelligently select the most appropriate behavioral characteristics given the perceived operating environment. Subsequently, Modular control module reduces the search space of overtaking trajectories through an SVM based learning approach. These trajectories are then examined for possible future time collision using Velocity Obstacle. It employs non linear time scaling that provides for continuous trajectories in the space of linear and angular velocities to achieve continuous curvature overtaking maneuvers respecting velocity and acceleration bounds. Further time scaling also can scale velocities to avoid collisions and can compute a time optimal trajectory for the learned behavior. The preliminary results show the appropriateness of our proposed framework in virtual urban environment.
{"title":"Overtaking maneuvers by non linear time scaling over reduced set of learned motion primitives","authors":"Vishakh Duggal, K. Bipin, Ashutosh Kumar Singh, Bharath Gopalakrishnan, B. Bharti, A. Khiat, K. Krishna","doi":"10.1109/IVS.2015.7225672","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225672","url":null,"abstract":"Overtaking of a vehicle moving on structured roads is one of the most frequent driving behavior. In this work, we have described a Real Time Control System based framework for overtaking maneuver of autonomous vehicles. Proposed framework incorporates Intelligent Planning and Modular control modules. Intelligent Planning module of the framework enables the vehicle to intelligently select the most appropriate behavioral characteristics given the perceived operating environment. Subsequently, Modular control module reduces the search space of overtaking trajectories through an SVM based learning approach. These trajectories are then examined for possible future time collision using Velocity Obstacle. It employs non linear time scaling that provides for continuous trajectories in the space of linear and angular velocities to achieve continuous curvature overtaking maneuvers respecting velocity and acceleration bounds. Further time scaling also can scale velocities to avoid collisions and can compute a time optimal trajectory for the learned behavior. The preliminary results show the appropriateness of our proposed framework in virtual urban environment.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128505758","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225840
Xiaohui Li, Zhenping Sun, Zhen He, Q. Zhu, Daxue Liu
This paper presents a practical trajectory planning framework towards fully autonomous driving in urban environments. Firstly, based on the behavioral decision commands, a reference path is extracted from the digital map using the LIDAR-based localization information. The reference path is refined and interpolated via a nonlinear optimization algorithm and a parametric algorithm, respectively. Secondly, the trajectory planning task is decomposed into spatial path planning and velocity profile planning. A closed-form algorithm is employed to generate a rich set of kinematically-feasible spatial path candidates within the curvilinear coordinate framework. At the same time, the velocity planning algorithm is performed with considering safety and smoothness constraints. The trajectory candidates are evaluated by a carefully developed objective function. Subsequently, the best collision-free and dynamically-feasible trajectory is selected and executed by the trajectory tracking controller. We implemented the proposed trajectory planning strategy on our test autonomous vehicle in the realistic urban traffic scenarios. Experimental results demonstrated its capability and efficiency to handle a variety of driving situations, such as lane keeping, lane changing, vehicle following, and static and dynamic obstacles avoiding, while respecting traffic regulations.
{"title":"A practical trajectory planning framework for autonomous ground vehicles driving in urban environments","authors":"Xiaohui Li, Zhenping Sun, Zhen He, Q. Zhu, Daxue Liu","doi":"10.1109/IVS.2015.7225840","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225840","url":null,"abstract":"This paper presents a practical trajectory planning framework towards fully autonomous driving in urban environments. Firstly, based on the behavioral decision commands, a reference path is extracted from the digital map using the LIDAR-based localization information. The reference path is refined and interpolated via a nonlinear optimization algorithm and a parametric algorithm, respectively. Secondly, the trajectory planning task is decomposed into spatial path planning and velocity profile planning. A closed-form algorithm is employed to generate a rich set of kinematically-feasible spatial path candidates within the curvilinear coordinate framework. At the same time, the velocity planning algorithm is performed with considering safety and smoothness constraints. The trajectory candidates are evaluated by a carefully developed objective function. Subsequently, the best collision-free and dynamically-feasible trajectory is selected and executed by the trajectory tracking controller. We implemented the proposed trajectory planning strategy on our test autonomous vehicle in the realistic urban traffic scenarios. Experimental results demonstrated its capability and efficiency to handle a variety of driving situations, such as lane keeping, lane changing, vehicle following, and static and dynamic obstacles avoiding, while respecting traffic regulations.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130192253","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225779
M. Haberl, M. Fellendorf
Several European cities need to reduce traffic related emissions in order to meet the European Directive 2008/50 on ambient air quality. In order to achieve these reductions diverse concepts to reduce traffic-related pollution do exist. The introduction of single measures in an urban region is often not sufficient. Hence, multiple concepts must be combined. The main focus of this work is to integrate emissions caused by individual and public traffic as a direct and explicit objective for a local adaptive signal-control optimization to guarantee emission-minimizing signalization. The second aim is the introduction of tactical driving for public transport using V2I communication to increase efficiency of traffic flow resulting from additional information for the drivers. The third goal is to quantify the influence of the driving style on fuel consumption and battery wear of a parallel hybrid city bus, leading to eco-driving.
{"title":"Eco-driven signal control and eco-driving of hybrid city buses","authors":"M. Haberl, M. Fellendorf","doi":"10.1109/IVS.2015.7225779","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225779","url":null,"abstract":"Several European cities need to reduce traffic related emissions in order to meet the European Directive 2008/50 on ambient air quality. In order to achieve these reductions diverse concepts to reduce traffic-related pollution do exist. The introduction of single measures in an urban region is often not sufficient. Hence, multiple concepts must be combined. The main focus of this work is to integrate emissions caused by individual and public traffic as a direct and explicit objective for a local adaptive signal-control optimization to guarantee emission-minimizing signalization. The second aim is the introduction of tactical driving for public transport using V2I communication to increase efficiency of traffic flow resulting from additional information for the drivers. The third goal is to quantify the influence of the driving style on fuel consumption and battery wear of a parallel hybrid city bus, leading to eco-driving.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128846923","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225816
S. Formentin, Andrea G. Bianchessi, S. Savaresi
The free-floating car sharing model is a recently introduced vehicle rental model, which allows customers to return the car anywhere within the operation area, without relying on depot stations. Driven by the flexibility of such a model, the popularity of car sharing has increased rapidly during the last years. However, some critical issues still arise when a user needs to make plans of vehicle usage, since no information is available on future vehicle locations. In this paper, the Vehicle Distance Prediction (VDP) approach is proposed, aimed to predict the distance of the nearest available vehicle at a given future instant. This technique shows great potential also for the service manager, e.g. vehicles could be moved in advance by the staff to balance the fleet distribution. The effectiveness of the proposed prediction approach is assessed on a real dataset taken from a car sharing service in Milan, Italy.
{"title":"On the prediction of future vehicle locations in free-floating car sharing systems","authors":"S. Formentin, Andrea G. Bianchessi, S. Savaresi","doi":"10.1109/IVS.2015.7225816","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225816","url":null,"abstract":"The free-floating car sharing model is a recently introduced vehicle rental model, which allows customers to return the car anywhere within the operation area, without relying on depot stations. Driven by the flexibility of such a model, the popularity of car sharing has increased rapidly during the last years. However, some critical issues still arise when a user needs to make plans of vehicle usage, since no information is available on future vehicle locations. In this paper, the Vehicle Distance Prediction (VDP) approach is proposed, aimed to predict the distance of the nearest available vehicle at a given future instant. This technique shows great potential also for the service manager, e.g. vehicles could be moved in advance by the staff to balance the fleet distribution. The effectiveness of the proposed prediction approach is assessed on a real dataset taken from a car sharing service in Milan, Italy.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123787346","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225737
Mario Passani, J. J. Torres, L. Bergasa
The future of autonomous vehicles and driver assistance systems is underpinned by the need of fast and efficient approaches for road scene understanding. Despite the large explored paths for road detection, there is still a research gap for incorporating image understanding capabilities in intelligent vehicles. This paper presents a pixelwise segmentation of roads from monocular images. The proposal is based on a probabilistic graphical model and a set of algorithms and configurations chosen to speed up the inference of the road pixels. In brief, the proposed method employs Conditional Random Fields and Uniformly Reweighted Belief Propagation. Besides, the approach is ranked on the KITTI ROAD dataset yielding state-of-the-art results with the lowest runtime per image using a standard PC.
{"title":"Fast pixelwise road inference based on Uniformly Reweighted Belief Propagation","authors":"Mario Passani, J. J. Torres, L. Bergasa","doi":"10.1109/IVS.2015.7225737","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225737","url":null,"abstract":"The future of autonomous vehicles and driver assistance systems is underpinned by the need of fast and efficient approaches for road scene understanding. Despite the large explored paths for road detection, there is still a research gap for incorporating image understanding capabilities in intelligent vehicles. This paper presents a pixelwise segmentation of roads from monocular images. The proposal is based on a probabilistic graphical model and a set of algorithms and configurations chosen to speed up the inference of the road pixels. In brief, the proposed method employs Conditional Random Fields and Uniformly Reweighted Belief Propagation. Besides, the approach is ranked on the KITTI ROAD dataset yielding state-of-the-art results with the lowest runtime per image using a standard PC.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"63 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120923615","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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