Pub Date : 2015-08-27DOI: 10.1109/IVS.2015.7225684
J. Suhr, H. Jung
This paper proposes a fast method for detecting symbolic road markings (SRMs) and stop-lines. The proposed method efficiently restricts the search area based on the lane detection results and finds SRMs and stop-lines in a cost-effective manner. The SRM detector generates multiple SRM candidates using a top-hat filter and projection histogram and classifies their types using a histogram of oriented gradient (HOG) feature and total error rate (TER)-based classifier. The stop-line detector creates stop-line candidates via random sample consensus (RANSAC)-based parallel line pair estimation and verifies them using the HOG feature and TER-based classifier. The proposed method achieves reasonable detection rates and extremely low false positive rates along with a fast computing time.
{"title":"Fast symbolic road marking and stop-line detection for vehicle localization","authors":"J. Suhr, H. Jung","doi":"10.1109/IVS.2015.7225684","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225684","url":null,"abstract":"This paper proposes a fast method for detecting symbolic road markings (SRMs) and stop-lines. The proposed method efficiently restricts the search area based on the lane detection results and finds SRMs and stop-lines in a cost-effective manner. The SRM detector generates multiple SRM candidates using a top-hat filter and projection histogram and classifies their types using a histogram of oriented gradient (HOG) feature and total error rate (TER)-based classifier. The stop-line detector creates stop-line candidates via random sample consensus (RANSAC)-based parallel line pair estimation and verifies them using the HOG feature and TER-based classifier. The proposed method achieves reasonable detection rates and extremely low false positive rates along with a fast computing time.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"83 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":"121377002","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.2015.7225855
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.2015.7225840
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.2015.7225766
Panrang Qu, Jianru Xue, Liang Ma, Chao Ma
The Vector Field Histogram (VFH) is a classical motion planning algorithm which is widely used to handle the trajectory planning problem of mobile robots. However, the traditional VFH algorithm is rarely applied to autonomous vehicles due to the vehicle's well-known non-holonomic constraints, especially in urban environments. To address this problem, we propose a constrained VFH algorithm which takes both kinematic and dynamic constraints of the vehicle into consideration. The goal is achieved via two contributions that concern both kinematic and dynamic constraints of the vehicle. First, we develop a new active region for VFH to guarantee that all states within the region are reachable for the vehicle. Second, we improve the cost function to guide the search to favor feasible motion direction for the vehicle. The proposed algorithm is extensively tested in various simulated urban environments, and experimental results validate its efficiency.
矢量场直方图(Vector Field Histogram, VFH)是一种经典的运动规划算法，被广泛用于处理移动机器人的轨迹规划问题。然而，由于众所周知的车辆非完整约束，特别是在城市环境中，传统的VFH算法很少应用于自动驾驶汽车。为了解决这一问题，我们提出了一种同时考虑车辆运动学和动力学约束的约束VFH算法。该目标是通过两个贡献来实现的，这两个贡献涉及车辆的运动学和动力学约束。首先，我们开发了一个新的VFH活动区域，以保证车辆在该区域内的所有状态都是可达的。其次，对代价函数进行改进，使搜索过程更有利于车辆的可行运动方向。在各种模拟城市环境中对该算法进行了广泛的测试，实验结果验证了该算法的有效性。
{"title":"A constrained VFH algorithm for motion planning of autonomous vehicles","authors":"Panrang Qu, Jianru Xue, Liang Ma, Chao Ma","doi":"10.1109/IVS.2015.7225766","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225766","url":null,"abstract":"The Vector Field Histogram (VFH) is a classical motion planning algorithm which is widely used to handle the trajectory planning problem of mobile robots. However, the traditional VFH algorithm is rarely applied to autonomous vehicles due to the vehicle's well-known non-holonomic constraints, especially in urban environments. To address this problem, we propose a constrained VFH algorithm which takes both kinematic and dynamic constraints of the vehicle into consideration. The goal is achieved via two contributions that concern both kinematic and dynamic constraints of the vehicle. First, we develop a new active region for VFH to guarantee that all states within the region are reachable for the vehicle. Second, we improve the cost function to guide the search to favor feasible motion direction for the vehicle. The proposed algorithm is extensively tested in various simulated urban environments, and experimental results validate its efficiency.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"30 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":"124057862","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.2015.7225796
Taewoo Kim, Jaewan Lee, K. Yi
This paper presents the maximum tire-road friction coefficient estimation algorithm which considers about the effect of states. Tire force information is an important factor for active safety system. However, it is difficult to estimate due to the dependency on many states such as vehicle speed, tire pressure, and tire wear. In this paper, several experimental researches about the effect of states on the maximum friction coefficient and previous maximum tire-road friction coefficient estimation algorithms are reviewed and summarized. The influential states and the estimation method which doesn't require extra sensors were determined and combined. The proposed algorithm consists of two parts: an interacting multiple models (IMM) based maximum tire-road friction coefficient estimation and an updating sequence based on the effect of vehicle speed. To validate the algorithm, the closed-loop simulation with the advanced emergency braking system (AEBS) has been conducted. It has been shown that the proposed estimation algorithm could enhance the performance of AEBS algorithm.
{"title":"Enhanced maximum tire-road friction coefficient estimation based advanced emergency braking algorithm","authors":"Taewoo Kim, Jaewan Lee, K. Yi","doi":"10.1109/IVS.2015.7225796","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225796","url":null,"abstract":"This paper presents the maximum tire-road friction coefficient estimation algorithm which considers about the effect of states. Tire force information is an important factor for active safety system. However, it is difficult to estimate due to the dependency on many states such as vehicle speed, tire pressure, and tire wear. In this paper, several experimental researches about the effect of states on the maximum friction coefficient and previous maximum tire-road friction coefficient estimation algorithms are reviewed and summarized. The influential states and the estimation method which doesn't require extra sensors were determined and combined. The proposed algorithm consists of two parts: an interacting multiple models (IMM) based maximum tire-road friction coefficient estimation and an updating sequence based on the effect of vehicle speed. To validate the algorithm, the closed-loop simulation with the advanced emergency braking system (AEBS) has been conducted. It has been shown that the proposed estimation algorithm could enhance the performance of AEBS algorithm.","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":"127816196","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.2015.7225904
Donghoon Shin, K. Yi
For the generic assessment and the total management of collision risks in urban driving situations, it is important to estimate and represent the target vehicles' behavior such as yaw rate, absolute velocity and acceleration which are state of the target vehicle. To achieve this, this paper presents a compensation of wireless communication delay for integrated risk management of automated vehicle. Recent developments in vehicle onboard computers and wireless communications devices, also known as dedicated short-range communication (DSRC) devices allow the exchange of information between vehicles (inter-vehicle communications). In an application of vehicle to vehicle (V2V) communication, the most important issue is to handle delay which has a negative impact on safety issue since communication networks generally introduce delays. To cope with this problem, the inter-vehicle communication system is firstly modelled by reflecting signal characteristic. To compensate the communication delay, state augmented estimation algorithm is used based on extended Kalman filters (EKF). The performance of the proposed estimation algorithm is verified via real time simulations.
{"title":"Compensation of wireless communication delay for integrated risk management of automated vehicle","authors":"Donghoon Shin, K. Yi","doi":"10.1109/IVS.2015.7225904","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225904","url":null,"abstract":"For the generic assessment and the total management of collision risks in urban driving situations, it is important to estimate and represent the target vehicles' behavior such as yaw rate, absolute velocity and acceleration which are state of the target vehicle. To achieve this, this paper presents a compensation of wireless communication delay for integrated risk management of automated vehicle. Recent developments in vehicle onboard computers and wireless communications devices, also known as dedicated short-range communication (DSRC) devices allow the exchange of information between vehicles (inter-vehicle communications). In an application of vehicle to vehicle (V2V) communication, the most important issue is to handle delay which has a negative impact on safety issue since communication networks generally introduce delays. To cope with this problem, the inter-vehicle communication system is firstly modelled by reflecting signal characteristic. To compensate the communication delay, state augmented estimation algorithm is used based on extended Kalman filters (EKF). The performance of the proposed estimation algorithm is verified via real time simulations.","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":"127865101","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.2015.7225786
L. Horne, J. Álvarez, M. Salzmann, N. Barnes
Efficient, fully-connected CRF inference enables fast semantic labelling of images. However, this requires high-quality unary potentials to be computed, which is currently time-consuming. While some recent work attempts to address this issue by only computing a subset of unary potentials, a need remains for a simple, fast way to decide which unary potentials should be computed, without sacrificing accuracy. In particular, for embedded applications, a method which avoids time or memory-intensive operations is desired. In this paper, we introduce an approach to selecting good locations to compute unary potentials. We implement an efficient morphological approach to select a small proportion of pixel locations where unary potentials will be calculated. The speed of our labelling method allows us to directly search a large parameter space to optimize our method for a given task. We show that our method can achieve comparable accuracy to what can be achieved when all unary potentials are calculated, with significant time saving. Furthermore, we show that it is possible to tune our method to yield improved accuracy for certain classes of interest. We demonstrate this over multiple datasets representing challenging applications for our approach.
{"title":"Efficient scene parsing by sampling unary potentials in a fully-connected CRF","authors":"L. Horne, J. Álvarez, M. Salzmann, N. Barnes","doi":"10.1109/IVS.2015.7225786","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225786","url":null,"abstract":"Efficient, fully-connected CRF inference enables fast semantic labelling of images. However, this requires high-quality unary potentials to be computed, which is currently time-consuming. While some recent work attempts to address this issue by only computing a subset of unary potentials, a need remains for a simple, fast way to decide which unary potentials should be computed, without sacrificing accuracy. In particular, for embedded applications, a method which avoids time or memory-intensive operations is desired. In this paper, we introduce an approach to selecting good locations to compute unary potentials. We implement an efficient morphological approach to select a small proportion of pixel locations where unary potentials will be calculated. The speed of our labelling method allows us to directly search a large parameter space to optimize our method for a given task. We show that our method can achieve comparable accuracy to what can be achieved when all unary potentials are calculated, with significant time saving. Furthermore, we show that it is possible to tune our method to yield improved accuracy for certain classes of interest. We demonstrate this over multiple datasets representing challenging applications for our approach.","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":"125397708","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.2015.7225807
Kei Tateiwa, K. Yamada
This paper presents a method for estimating a driver's awareness of the presence of a pedestrian while crossing or trying to cross a crosswalk located in the path of a right or left turn when the driver is trying to turn right or left at an intersection based on the behavior of the vehicle operated by the driver. The method is based on the idea that different driving behaviors occur in similar situations depending on whether the driver notices a pedestrian. The results of an evaluation performed using actual driving behavior data of vehicles driven on public roads are also reported.
{"title":"Estimating driver awareness of pedestrians in crosswalk in the path of right or left turns at an intersection from vehicle behavior","authors":"Kei Tateiwa, K. Yamada","doi":"10.1109/IVS.2015.7225807","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225807","url":null,"abstract":"This paper presents a method for estimating a driver's awareness of the presence of a pedestrian while crossing or trying to cross a crosswalk located in the path of a right or left turn when the driver is trying to turn right or left at an intersection based on the behavior of the vehicle operated by the driver. The method is based on the idea that different driving behaviors occur in similar situations depending on whether the driver notices a pedestrian. The results of an evaluation performed using actual driving behavior data of vehicles driven on public roads are also reported.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"43 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":"127259502","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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