Pub Date : 2015-08-27DOI: 10.1109/IVS.2015.7225814
S. Amritha, S. Subramanian, L. Vanajakshi
Traffic density, defined as the number of vehicles per unit length, is the primary measure used for quantifying road congestion. However, the direct measurement of this variable is difficult due to its spatial nature and the only method to directly measure it from field is aerial photography. Hence, it is usually estimated from other easily measurable variables such as speed or flow. Some of the reported approaches to obtain density include the input output analysis, fundamental traffic flow relation, and occupancy-based measurements in addition to those based on statistics, machine learning or model-based approaches. However, for better performance, all these methods require the careful selection of the relevant input variables/parameters and their relationships. One way of obtaining these relationships is to perform a dimensional analysis of the variables/parameters involved, identifying the non-dimensional variables/parameters and then obtaining a relationship between them using experimental data. This approach has been attempted for estimating road traffic density in this paper. The appropriate non-dimensional variables/parameters that characterize road traffic flow were first determined and the relation between them was then found out using simulated data. This relationship was subsequently used to estimate density for other datasets and the results were found to be promising.
{"title":"Traffic density estimation using dimensional analysis","authors":"S. Amritha, S. Subramanian, L. Vanajakshi","doi":"10.1109/IVS.2015.7225814","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225814","url":null,"abstract":"Traffic density, defined as the number of vehicles per unit length, is the primary measure used for quantifying road congestion. However, the direct measurement of this variable is difficult due to its spatial nature and the only method to directly measure it from field is aerial photography. Hence, it is usually estimated from other easily measurable variables such as speed or flow. Some of the reported approaches to obtain density include the input output analysis, fundamental traffic flow relation, and occupancy-based measurements in addition to those based on statistics, machine learning or model-based approaches. However, for better performance, all these methods require the careful selection of the relevant input variables/parameters and their relationships. One way of obtaining these relationships is to perform a dimensional analysis of the variables/parameters involved, identifying the non-dimensional variables/parameters and then obtaining a relationship between them using experimental data. This approach has been attempted for estimating road traffic density in this paper. The appropriate non-dimensional variables/parameters that characterize road traffic flow were first determined and the relation between them was then found out using simulated data. This relationship was subsequently used to estimate density for other datasets and the results were found to be promising.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"42 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":"115612668","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.2015.7225666
Yue Kang, Danilo Alves de Lima, A. Victorino
This paper presents an approach for the development of Advanced Driving Assistance System (ADAS) based on the human-vehicle interaction using Image-based Dynamic Window Approach (IDWA). The IDWA is associated to a method for dynamic obstacles avoidance in order to prevent human driving errors, in the context of intelligent robotic vehicles. The human-vehicle interaction is presented by the correction of the Human Driving Behavior (HDB) controller for driving defaults of human drivers, with respect to referential paths that intimate the average driving path in real circumstances. The performance of the proposed human-vehicle interaction methodology, based on autonomous embedded functionalities, is simulated and verified in different bypass scenarios.
{"title":"Dynamic obstacles avoidance based on image-based dynamic window approach for human-vehicle interaction","authors":"Yue Kang, Danilo Alves de Lima, A. Victorino","doi":"10.1109/IVS.2015.7225666","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225666","url":null,"abstract":"This paper presents an approach for the development of Advanced Driving Assistance System (ADAS) based on the human-vehicle interaction using Image-based Dynamic Window Approach (IDWA). The IDWA is associated to a method for dynamic obstacles avoidance in order to prevent human driving errors, in the context of intelligent robotic vehicles. The human-vehicle interaction is presented by the correction of the Human Driving Behavior (HDB) controller for driving defaults of human drivers, with respect to referential paths that intimate the average driving path in real circumstances. The performance of the proposed human-vehicle interaction methodology, based on autonomous embedded functionalities, is simulated and verified in different bypass scenarios.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"66 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":"115258004","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.2015.7225693
Tongtong Chen, B. Dai, Daxue Liu, Jinze Song, Zhao Liu
Long range curb detection is crucial for an Autonomous Land Vehicle (ALV) navigation in urban environments. This paper presents a novel curb detection algorithm which can detect the curbs up to 50 meters away with Velodyne LIDAR. Instead of building a Digital Elevation Map (DEM) and utilizing geometric features (like normal direction) to extract candidate curb points, we take each scan line of Velodyne LIDAR as a processing unite directly. Some feature points, which are extracted from individual scan lines, are selected as the initial curb points by the distance criterion and Hough Transform (HT). Eventually, iterative Gaussian Process Regression (GPR), which utilizes the above initial curb points as the initial seeds, is exploited to represent both the curved and straight-line curb model. In order to verify the effectiveness of our algorithm quantitatively, 2934 Velodyne scans are collected in various urban scenes with our ALV, and 566 of them are labelled manually1. Our algorithm is also compared with two other curb detection techniques. The experimental results on the dataset show promising performance.
{"title":"Velodyne-based curb detection up to 50 meters away","authors":"Tongtong Chen, B. Dai, Daxue Liu, Jinze Song, Zhao Liu","doi":"10.1109/IVS.2015.7225693","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225693","url":null,"abstract":"Long range curb detection is crucial for an Autonomous Land Vehicle (ALV) navigation in urban environments. This paper presents a novel curb detection algorithm which can detect the curbs up to 50 meters away with Velodyne LIDAR. Instead of building a Digital Elevation Map (DEM) and utilizing geometric features (like normal direction) to extract candidate curb points, we take each scan line of Velodyne LIDAR as a processing unite directly. Some feature points, which are extracted from individual scan lines, are selected as the initial curb points by the distance criterion and Hough Transform (HT). Eventually, iterative Gaussian Process Regression (GPR), which utilizes the above initial curb points as the initial seeds, is exploited to represent both the curved and straight-line curb model. In order to verify the effectiveness of our algorithm quantitatively, 2934 Velodyne scans are collected in various urban scenes with our ALV, and 566 of them are labelled manually1. Our algorithm is also compared with two other curb detection techniques. The experimental results on the dataset show promising performance.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"207 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":"115917854","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.2015.7225806
Yuan Liao, S. Li, Wenjun Wang, Y. Wang, Guofa Li, B. Cheng
Driver distraction has been identified as an important driving safety issue. However, existed studies focused less on low-speed condition, especially at intersections. This paper aims to find the impact of driver cognitive distraction on vehicle performance at stop-controlled intersections. Eight subjects (young adult: 4, older adult: 4) participated in this study and each of them drove through 40 stop-controlled intersections. The intersections were presented randomly at two levels of FOV (field of view). Driver cognitive distraction was induced by a one-back task and a clock task. Results showed that the cognitive tasks led to more abrupt steering in both age groups while significant influence on lane-keeping capability was only observed in the young group. Steering smoothness was mainly influenced by the cognitive tasks at brake on-restart phase in the young group while at after-restart phase in the older group. Impaired longitudinal control (stop for watching) was observed in the older adult group. These findings can be applied to automatically recognize driver distraction at stop-controlled intersections in future.
{"title":"The impact of driver cognitive distraction on vehicle performance at stop-controlled intersections","authors":"Yuan Liao, S. Li, Wenjun Wang, Y. Wang, Guofa Li, B. Cheng","doi":"10.1109/IVS.2015.7225806","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225806","url":null,"abstract":"Driver distraction has been identified as an important driving safety issue. However, existed studies focused less on low-speed condition, especially at intersections. This paper aims to find the impact of driver cognitive distraction on vehicle performance at stop-controlled intersections. Eight subjects (young adult: 4, older adult: 4) participated in this study and each of them drove through 40 stop-controlled intersections. The intersections were presented randomly at two levels of FOV (field of view). Driver cognitive distraction was induced by a one-back task and a clock task. Results showed that the cognitive tasks led to more abrupt steering in both age groups while significant influence on lane-keeping capability was only observed in the young group. Steering smoothness was mainly influenced by the cognitive tasks at brake on-restart phase in the young group while at after-restart phase in the older group. Impaired longitudinal control (stop for watching) was observed in the older adult group. These findings can be applied to automatically recognize driver distraction at stop-controlled intersections in future.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"37 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":"115001919","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.2015.7225734
H. Oh, Cunjia Liu, Seungkeun Kim, Hyo-Sang Shin, Wen‐Hua Chen
This paper presents a new standoff tracking framework of a moving ground target using UAVs with a limited sensing capability such as sensor field-of-view and motion constraints. To maintain persistent track of the target even in case of target loss (out of surveillance) for a certain period, this study predicts the target existence area using the particle filter, and produces control commands to ensure that all predicted particles can be covered by the field-of-view of the UAV sensor at all times. To improve target prediction/estimation accuracy, the road information is incorporated into the constrained particle filter where the road boundaries are modelled as nonlinear inequality constraints. Both Lyapunov vector field guidance and nonlinear model predictive control methods are applied for the standoff tracking and phase angle control, and the advantages and disadvantages of them are compared using numerical simulation results.
{"title":"Coordinated standoff tracking of in- and out-of-surveillance targets using constrained particle filter for UAVs","authors":"H. Oh, Cunjia Liu, Seungkeun Kim, Hyo-Sang Shin, Wen‐Hua Chen","doi":"10.1109/IVS.2015.7225734","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225734","url":null,"abstract":"This paper presents a new standoff tracking framework of a moving ground target using UAVs with a limited sensing capability such as sensor field-of-view and motion constraints. To maintain persistent track of the target even in case of target loss (out of surveillance) for a certain period, this study predicts the target existence area using the particle filter, and produces control commands to ensure that all predicted particles can be covered by the field-of-view of the UAV sensor at all times. To improve target prediction/estimation accuracy, the road information is incorporated into the constrained particle filter where the road boundaries are modelled as nonlinear inequality constraints. Both Lyapunov vector field guidance and nonlinear model predictive control methods are applied for the standoff tracking and phase angle control, and the advantages and disadvantages of them are compared using numerical simulation results.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"40 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":"123512042","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.2015.7225728
Valentin Magnier, D. Gruyer, J. Godelle
This paper aims to present how the Belief Theory (also known as the Dempster-Shafer theory) can be relevant to implement powerful tracking systems. As the Belief theory belongs to the group of information-theories, it is very suitable for solving the track-to-target association problem which is one of the main issue of tracking systems. The data association problem is about associating the measurement at a given time-stamp with the objects that are being tracked along the time. In this paper, we present two methods based on the belief theory that can be used to improve the data association reliability. After that we propose as an example of tracking implementation and discuss the effect of using a multi-criteria track-to-target association algorithm.
{"title":"Implementation of a multi-criteria tracking based on the dempster-Shafer theory","authors":"Valentin Magnier, D. Gruyer, J. Godelle","doi":"10.1109/IVS.2015.7225728","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225728","url":null,"abstract":"This paper aims to present how the Belief Theory (also known as the Dempster-Shafer theory) can be relevant to implement powerful tracking systems. As the Belief theory belongs to the group of information-theories, it is very suitable for solving the track-to-target association problem which is one of the main issue of tracking systems. The data association problem is about associating the measurement at a given time-stamp with the objects that are being tracked along the time. In this paper, we present two methods based on the belief theory that can be used to improve the data association reliability. After that we propose as an example of tracking implementation and discuss the effect of using a multi-criteria track-to-target association algorithm.","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":"115172544","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.2015.7225800
M. Fu, Kai Zhang, Yi Yang, Hao Zhu, Meiling Wang
For the local path planning problem of autonomous vehicle in a complicated environment, a method combining cubic hermite spline curves with the kinematic model of autonomous vehicle is developed. And a novel algorithm for obstacle avoidance, called navigation circle, is proposed to take the road structure into account, which is a practical method for real-time path planning. In the new method, one of the trajectory generated by cubic hermite spline curves or navigation circle is optimized through the kinematic model of autonomous vehicle to get the kinematically feasible trajectory. The optimization is actually a numerical forward propagation and is easy to implement. The simulation experiment is conducted on the Robot Operating System (ROS) platform, which is based on replaying the data of the real world obtained from sensors or other modules on autonomous vehicle. Satisfactory simulation results verify the validity and the efficiency of the proposed method as well as the planner's capability to navigate in a realistic scenario.
{"title":"Collision-free and kinematically feasible path planning along a reference path for autonomous vehicle","authors":"M. Fu, Kai Zhang, Yi Yang, Hao Zhu, Meiling Wang","doi":"10.1109/IVS.2015.7225800","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225800","url":null,"abstract":"For the local path planning problem of autonomous vehicle in a complicated environment, a method combining cubic hermite spline curves with the kinematic model of autonomous vehicle is developed. And a novel algorithm for obstacle avoidance, called navigation circle, is proposed to take the road structure into account, which is a practical method for real-time path planning. In the new method, one of the trajectory generated by cubic hermite spline curves or navigation circle is optimized through the kinematic model of autonomous vehicle to get the kinematically feasible trajectory. The optimization is actually a numerical forward propagation and is easy to implement. The simulation experiment is conducted on the Robot Operating System (ROS) platform, which is based on replaying the data of the real world obtained from sensors or other modules on autonomous vehicle. Satisfactory simulation results verify the validity and the efficiency of the proposed method as well as the planner's capability to navigate in a realistic scenario.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"48 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":"115649064","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.2015.7225709
Arto J. Niskanen, A. Tuononen
Ever increasing amount of Advanced Driver Assisting Systems (ADAS) are being developed to improve the safety of the vehicles. However, the direct information of the tyre-road contact is not available for these systems. An intelligent tyre has been proposed to provide that information in many studies. This paper focuses on water and aquaplaning detection with an intelligent tyre with three triaxial accelerometers attached on the inner liner. A method to detect the tyre-road contact state is introduced and validated with experimental data.
{"title":"Accelerometer tyre to estimate the aquaplaning state of the tyre-road contact","authors":"Arto J. Niskanen, A. Tuononen","doi":"10.1109/IVS.2015.7225709","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225709","url":null,"abstract":"Ever increasing amount of Advanced Driver Assisting Systems (ADAS) are being developed to improve the safety of the vehicles. However, the direct information of the tyre-road contact is not available for these systems. An intelligent tyre has been proposed to provide that information in many studies. This paper focuses on water and aquaplaning detection with an intelligent tyre with three triaxial accelerometers attached on the inner liner. A method to detect the tyre-road contact state is introduced and validated with experimental data.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"35 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":"127868077","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.2015.7225765
A. Broggi, S. Debattisti, P. Grisleri, M. Panciroli
This paper presents the design, the setup, and the architecture of a new class of autonomous vehicles prototype. The car is equipped with 26 cameras, divided into 13 stereo pairs. Four stereo systems are dedicated to the reconstruction of the near area surrounding the vehicle, other nine are dedicated to the 3D reconstruction of the far driving area. Additionally four laserscanners, and a high performance GPS/IMU unit provide more information on the ground truth to measure the validity of the data obtained with vision. The autonomous driving system, the perception system, and the processing system have been installed in the vehicle taking extra care for an as-clean-as-possible integration with te aim of setting a new state of the art level for this kind of setup. A Human Machine Interface, allows the driver to control all the functions of the systems using a touch screen. A high precision synchronization system coupled with a custom software architecture allows to obtain recordings from all sensors installed.
{"title":"The deeva autonomous vehicle platform","authors":"A. Broggi, S. Debattisti, P. Grisleri, M. Panciroli","doi":"10.1109/IVS.2015.7225765","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225765","url":null,"abstract":"This paper presents the design, the setup, and the architecture of a new class of autonomous vehicles prototype. The car is equipped with 26 cameras, divided into 13 stereo pairs. Four stereo systems are dedicated to the reconstruction of the near area surrounding the vehicle, other nine are dedicated to the 3D reconstruction of the far driving area. Additionally four laserscanners, and a high performance GPS/IMU unit provide more information on the ground truth to measure the validity of the data obtained with vision. The autonomous driving system, the perception system, and the processing system have been installed in the vehicle taking extra care for an as-clean-as-possible integration with te aim of setting a new state of the art level for this kind of setup. A Human Machine Interface, allows the driver to control all the functions of the systems using a touch screen. A high precision synchronization system coupled with a custom software architecture allows to obtain recordings from all sensors installed.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"80 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":"116578181","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.2015.7225783
Ji-Ho Cho, M. Humenberger
Due to recent developments of low-cost image sensors and high-performance embedded processing hardware, future cars and automotive systems will increasingly use binocular stereo vision for environmental perception. However, research and development in stereo vision is still ongoing since there are many challenges unsolved. In this paper, we propose a fast and accurate stereo matching algorithm, designed for automotive applications. It convincingly handles real-world scenes containing complex, textureless, and slanted surfaces. To achieve that, we propose an improved PatchMatch stereo algorithm that combines a census-based cost function with Semi-Global Matching optimization integrated in a cross-scale fusion processing scheme. To further accelerate the algorithm, we propose a novel enhancement approach for PatchMatch-based approximation which allows us to skip the random search or at least significantly reduce the number of iterations. Our method is ranked in the upper third of the KITTI benchmark and among the top performers in terms of processing time.
{"title":"Fast PatchMatch stereo matching using cross-scale cost fusion for automotive applications","authors":"Ji-Ho Cho, M. Humenberger","doi":"10.1109/IVS.2015.7225783","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225783","url":null,"abstract":"Due to recent developments of low-cost image sensors and high-performance embedded processing hardware, future cars and automotive systems will increasingly use binocular stereo vision for environmental perception. However, research and development in stereo vision is still ongoing since there are many challenges unsolved. In this paper, we propose a fast and accurate stereo matching algorithm, designed for automotive applications. It convincingly handles real-world scenes containing complex, textureless, and slanted surfaces. To achieve that, we propose an improved PatchMatch stereo algorithm that combines a census-based cost function with Semi-Global Matching optimization integrated in a cross-scale fusion processing scheme. To further accelerate the algorithm, we propose a novel enhancement approach for PatchMatch-based approximation which allows us to skip the random search or at least significantly reduce the number of iterations. Our method is ranked in the upper third of the KITTI benchmark and among the top performers in terms of processing time.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"54 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":"126653819","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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