Pub Date : 2015-08-27DOI: 10.1109/IVS.2015.7225665
Julian Thomas, Kai Stiens, Sebastian Rauch, R. Rojas
The information about the road course and individual lanes is an important requirement in driver assistance systems and for automated driving applications. It is often stored in a highly accurate offline map so that the road and the lanes are known in advance. However, there exist situations where an offline map can become unusable or invalid. This paper presents a novel approach for a road model estimation solely based on online measurements from sensors mounted on the ego vehicle. It combines perception data like detected lane markings, the movement history of dynamic objects in the vehicle's environment and detected road boundaries into a grid-based road model. This approach allows for an estimation of the road model even when one source of information is not available and offers a redundant source of information about the road, which is necessary in critical applications such as automated driving. The presented approach was tested and evaluated with a prototype vehicle and real sensor data from German highway scenarios.
{"title":"Grid-based online road model estimation for advanced driver assistance systems","authors":"Julian Thomas, Kai Stiens, Sebastian Rauch, R. Rojas","doi":"10.1109/IVS.2015.7225665","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225665","url":null,"abstract":"The information about the road course and individual lanes is an important requirement in driver assistance systems and for automated driving applications. It is often stored in a highly accurate offline map so that the road and the lanes are known in advance. However, there exist situations where an offline map can become unusable or invalid. This paper presents a novel approach for a road model estimation solely based on online measurements from sensors mounted on the ego vehicle. It combines perception data like detected lane markings, the movement history of dynamic objects in the vehicle's environment and detected road boundaries into a grid-based road model. This approach allows for an estimation of the road model even when one source of information is not available and offers a redundant source of information about the road, which is necessary in critical applications such as automated driving. The presented approach was tested and evaluated with a prototype vehicle and real sensor data from German highway scenarios.","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":"129730006","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.2015.7225717
Yongbon Koo, Jinwoo Kim, Wooyong Han
Many researchers have reported that a decline in driving concentration caused by drowsiness or inattentiveness is one of the primary sources of serious car accidents. One of the most well-known methods to measure a driver's concentration is called driver state monitoring, where the driver is warned when he or she is falling asleep based on visual information of the face. On the other hand, autonomous driving systems have garnered attention in recent years as an alternative plan to reduce human-caused accidents. This system shows the possibility of realizing a vehicle with no steering wheel or pedals. However, lack of technical maturity, human acceptance problems, and individual desire to drive highlight the demand to keep human drivers in the loop. For these reasons, it is necessary to decide who will be responsible for driving the vehicle and adjusting the vehicle control system. This is known as the driving control authority. In this paper, we present a system that can suggest transitions in various driving control authority modes by sensing a decline of the human driver's performance caused by drowsiness or inattentiveness. In more detail, we identify the problems of the legacy driving control authority transition made only with vision-based driver state recognition. To address the shortcomings of this method, we propose a new recommendation method that combines the vision-based driver state recognition results and path suggestion of an autonomous system. Experiment results of simulated drowsy and inattentive drivers on an actual autonomous vehicle prototype show that our method has better transition accuracy with fewer false-positive errors compared with the legacy transition method that only uses vision-based driver state recognition.
{"title":"A method for driving control authority transition for cooperative autonomous vehicle","authors":"Yongbon Koo, Jinwoo Kim, Wooyong Han","doi":"10.1109/IVS.2015.7225717","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225717","url":null,"abstract":"Many researchers have reported that a decline in driving concentration caused by drowsiness or inattentiveness is one of the primary sources of serious car accidents. One of the most well-known methods to measure a driver's concentration is called driver state monitoring, where the driver is warned when he or she is falling asleep based on visual information of the face. On the other hand, autonomous driving systems have garnered attention in recent years as an alternative plan to reduce human-caused accidents. This system shows the possibility of realizing a vehicle with no steering wheel or pedals. However, lack of technical maturity, human acceptance problems, and individual desire to drive highlight the demand to keep human drivers in the loop. For these reasons, it is necessary to decide who will be responsible for driving the vehicle and adjusting the vehicle control system. This is known as the driving control authority. In this paper, we present a system that can suggest transitions in various driving control authority modes by sensing a decline of the human driver's performance caused by drowsiness or inattentiveness. In more detail, we identify the problems of the legacy driving control authority transition made only with vision-based driver state recognition. To address the shortcomings of this method, we propose a new recommendation method that combines the vision-based driver state recognition results and path suggestion of an autonomous system. Experiment results of simulated drowsy and inattentive drivers on an actual autonomous vehicle prototype show that our method has better transition accuracy with fewer false-positive errors compared with the legacy transition method that only uses vision-based driver state recognition.","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":"127716452","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.2015.7225770
F. Flehmig, Amir Sardari, Uta Fischer, A. Wagner
Adaptive Cruise Control (ACC) automates longitudinal guidance of the vehicle. This paper presents a method to calculate energy optimal drive strategies when the longitudinal movement of the vehicle is constrained by another vehicle, i.e. when the ACC vehicle follows another slower vehicle. The A* Algorithm is employed for optimization and is shown to yield the optimal solution due to a suitable heuristics. Energy optimal drive strategies are calculated for some ACC use cases and their benefit is illustrated with measurements from test tracks, on public roads as well as with simulation of traffic scenarios as encountered on public roads.
{"title":"Energy optimal Adaptive Cruise Control during following of other vehicles","authors":"F. Flehmig, Amir Sardari, Uta Fischer, A. Wagner","doi":"10.1109/IVS.2015.7225770","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225770","url":null,"abstract":"Adaptive Cruise Control (ACC) automates longitudinal guidance of the vehicle. This paper presents a method to calculate energy optimal drive strategies when the longitudinal movement of the vehicle is constrained by another vehicle, i.e. when the ACC vehicle follows another slower vehicle. The A* Algorithm is employed for optimization and is shown to yield the optimal solution due to a suitable heuristics. Energy optimal drive strategies are calculated for some ACC use cases and their benefit is illustrated with measurements from test tracks, on public roads as well as with simulation of traffic scenarios as encountered on public roads.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"31 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":"128568811","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.2015.7225738
Keisuke Yoneda, Chenxi Yang, S. Mita, Tsubasa Okuya, K. Muto
In recent years, automated vehicle researches move on to the next stage, that is, auto-driving experiments on public roads. This study focuses on how to realize accurate localization based on the use of Lidar data and precise map. On different roads such as urban roads and expressways, the observed information of surrounding is significantly different. For example, on the urban roads, many buildings can be observed around the upper part of the vehicle. Such observation realizes accurate map matching. On the other hand, the upper part has no specific observation on the expressway. Therefore, it is necessary to observe the lower part for the map matching. To adapt the situation changes, we propose a localization method based on self-adaptive multi-layered scan matching and road line segment matching. The main idea is to effectively match the features observed from different heights and to improve the results by applying the line segment matching in certain scenes. Localization experiments show the ability to estimate accurate vehicle pose in urban driving.
{"title":"Urban road localization by using multiple layer map matching and line segment matching","authors":"Keisuke Yoneda, Chenxi Yang, S. Mita, Tsubasa Okuya, K. Muto","doi":"10.1109/IVS.2015.7225738","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225738","url":null,"abstract":"In recent years, automated vehicle researches move on to the next stage, that is, auto-driving experiments on public roads. This study focuses on how to realize accurate localization based on the use of Lidar data and precise map. On different roads such as urban roads and expressways, the observed information of surrounding is significantly different. For example, on the urban roads, many buildings can be observed around the upper part of the vehicle. Such observation realizes accurate map matching. On the other hand, the upper part has no specific observation on the expressway. Therefore, it is necessary to observe the lower part for the map matching. To adapt the situation changes, we propose a localization method based on self-adaptive multi-layered scan matching and road line segment matching. The main idea is to effectively match the features observed from different heights and to improve the results by applying the line segment matching in certain scenes. Localization experiments show the ability to estimate accurate vehicle pose in urban driving.","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":"129439959","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.2015.7225817
Ning Wu, Weiwei Huang, Zhiwei Song, Xiaojun Wu, Qun Zhang, Susu Yao
For autonomous navigation system of intelligent vehicle, robust and stable control with accurate tracking ability is one of the key requirements. In this paper, we present a systematic controller design approach for autonomous vehicle navigation system. The proposed controller integrates dynamic vehicle model and online updated path model by quadratic programming (QP) cost function, which considers both tracking error and stability. A novel path planner based on the differential dynamic programming (DDP) with consideration of the kinematic feasibility is used. The path tracking accuracy has been improved by utilizing the proposed dynamic preview controller. Promising experimental results showed that the overall navigation system is robust and stable.
{"title":"Adaptive dynamic preview control for autonomous vehicle trajectory following with DDP based path planner","authors":"Ning Wu, Weiwei Huang, Zhiwei Song, Xiaojun Wu, Qun Zhang, Susu Yao","doi":"10.1109/IVS.2015.7225817","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225817","url":null,"abstract":"For autonomous navigation system of intelligent vehicle, robust and stable control with accurate tracking ability is one of the key requirements. In this paper, we present a systematic controller design approach for autonomous vehicle navigation system. The proposed controller integrates dynamic vehicle model and online updated path model by quadratic programming (QP) cost function, which considers both tracking error and stability. A novel path planner based on the differential dynamic programming (DDP) with consideration of the kinematic feasibility is used. The path tracking accuracy has been improved by utilizing the proposed dynamic preview controller. Promising experimental results showed that the overall navigation system is robust and stable.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"31 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":"121939764","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.2015.7225767
Zhiwei Song, Weiwei Huang, Ning Wu, Xiaojun Wu, Chern Yuen Anthony Wong, V. B. Saputra, Benjamin Chia Hon Quan, Chen Jian Simon, Qun Zhang, Susu Yao, Boon Siew Han
This paper proposes a map free lane following solution based on low-cost 2D laser scanners for Autonomous Service Vehicle to fill the gap between future driverless car and the lane keeping assistant. The applications of autonomous service vehicle include feeder bus in a local residential area, shuttle bus in a park or playground, sprinkler car, sweeper car, and transporter in airport or container terminal. As autonomous service vehicle is running only in a limited area and its speed is slow compared to normal vehicles, we can further simplify the problem regardless of the issues of road infrastructure detection/communication and V2I maps which prevent the popularization of driverless car, and to propose a unique map free solution. The features of our approach include: 1) an innovative configuration for two 2D laser scanners to detect the lane with sharp curve; 2) a fast and accurate lane detection algorithm based on 2D laser's raw date directly; 3) a reliable and smooth path planning based on local lane fitting and prediction; and 4) a self-built unique drive-by-wire system for electronic car. We successfully tested our vehicle with autonomous driving in the testing field. The experiments show that the vehicle's trajectory matched the planned path accurately.
{"title":"Map free lane following based on low-cost laser scanner for near future autonomous service vehicle","authors":"Zhiwei Song, Weiwei Huang, Ning Wu, Xiaojun Wu, Chern Yuen Anthony Wong, V. B. Saputra, Benjamin Chia Hon Quan, Chen Jian Simon, Qun Zhang, Susu Yao, Boon Siew Han","doi":"10.1109/IVS.2015.7225767","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225767","url":null,"abstract":"This paper proposes a map free lane following solution based on low-cost 2D laser scanners for Autonomous Service Vehicle to fill the gap between future driverless car and the lane keeping assistant. The applications of autonomous service vehicle include feeder bus in a local residential area, shuttle bus in a park or playground, sprinkler car, sweeper car, and transporter in airport or container terminal. As autonomous service vehicle is running only in a limited area and its speed is slow compared to normal vehicles, we can further simplify the problem regardless of the issues of road infrastructure detection/communication and V2I maps which prevent the popularization of driverless car, and to propose a unique map free solution. The features of our approach include: 1) an innovative configuration for two 2D laser scanners to detect the lane with sharp curve; 2) a fast and accurate lane detection algorithm based on 2D laser's raw date directly; 3) a reliable and smooth path planning based on local lane fitting and prediction; and 4) a self-built unique drive-by-wire system for electronic car. We successfully tested our vehicle with autonomous driving in the testing field. The experiments show that the vehicle's trajectory matched the planned path accurately.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"10 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":"114263720","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.2015.7225780
A. Cabani, R. Khemmar, J. Ertaud, J. Mouzna
The aim of this work is to design and build by 2015 an electric four-seater equipped with an autonomous extension device. The project was born from two observations: in a context of necessary diversification of energy sources and the development of electric vehicles, the main problem remains the battery life and availability of charging stations. The issue of our work lies both in the optimization of energy consumption and improving the electric vehicle. Our team was tasked to develop and implement an Energy Management System of Electric Vehicle. The objective of the mission is to create a program that calculates the set speed to minimize the cost of energy consumption and maximize battery life. This calculation is done by taking into account prevention parameters are: vehicle speed, real-time parameters from Maps (elevations in the path, wind speed, etc.), the forces applied to the vehicle.
{"title":"Intelligent navigation system-based optimization of the energy consumption","authors":"A. Cabani, R. Khemmar, J. Ertaud, J. Mouzna","doi":"10.1109/IVS.2015.7225780","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225780","url":null,"abstract":"The aim of this work is to design and build by 2015 an electric four-seater equipped with an autonomous extension device. The project was born from two observations: in a context of necessary diversification of energy sources and the development of electric vehicles, the main problem remains the battery life and availability of charging stations. The issue of our work lies both in the optimization of energy consumption and improving the electric vehicle. Our team was tasked to develop and implement an Energy Management System of Electric Vehicle. The objective of the mission is to create a program that calculates the set speed to minimize the cost of energy consumption and maximize battery life. This calculation is done by taking into account prevention parameters are: vehicle speed, real-time parameters from Maps (elevations in the path, wind speed, etc.), the forces applied to the vehicle.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"141 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":"115254921","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.2015.7225827
Dominik Nuss, Ting Yuan, Gunther Krehl, M. Stuebler, Stephan Reuter, K. Dietmayer
Occupancy grid mapping is a well-known environment perception approach. A grid map divides the environment into cells and estimates the occupancy probability of each cell based on sensor measurements. An important extension is the Bayesian occupancy filter (BOF), which additionally estimates the dynamic state of grid cells and allows modeling changing environments. In recent years, the BOF attracted more and more attention, especially sequential Monte Carlo implementations (SMC-BOF), requiring less computational costs. An advantage compared to classical object tracking approaches is the object-free representation of arbitrarily shaped obstacles and free-space areas. Unfortunately, publications about BOF based on laser measurements report that grid cells representing big, contiguous, stationary obstacles are often mistaken as moving with the velocity of the ego vehicle (ghost movements). This paper presents a method to fuse laser and radar measurement data with the SMC-BOF. It shows that the doppler information of radar measurements significantly improves the dynamic estimation of the grid map, reduces ghost movements, and in general leads to a faster convergence of the dynamic estimation.
{"title":"Fusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter","authors":"Dominik Nuss, Ting Yuan, Gunther Krehl, M. Stuebler, Stephan Reuter, K. Dietmayer","doi":"10.1109/IVS.2015.7225827","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225827","url":null,"abstract":"Occupancy grid mapping is a well-known environment perception approach. A grid map divides the environment into cells and estimates the occupancy probability of each cell based on sensor measurements. An important extension is the Bayesian occupancy filter (BOF), which additionally estimates the dynamic state of grid cells and allows modeling changing environments. In recent years, the BOF attracted more and more attention, especially sequential Monte Carlo implementations (SMC-BOF), requiring less computational costs. An advantage compared to classical object tracking approaches is the object-free representation of arbitrarily shaped obstacles and free-space areas. Unfortunately, publications about BOF based on laser measurements report that grid cells representing big, contiguous, stationary obstacles are often mistaken as moving with the velocity of the ego vehicle (ghost movements). This paper presents a method to fuse laser and radar measurement data with the SMC-BOF. It shows that the doppler information of radar measurements significantly improves the dynamic estimation of the grid map, reduces ghost movements, and in general leads to a faster convergence of the dynamic estimation.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"404 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":"115340990","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.2015.7225653
M. Sasaki
On the basis of the recent technical trend of automated vehicles and connected vehicles, we have been proposing the new application concept Neuro-ITS which has three major technical features such as multi-viewpoint tracking, human appearance prediction, and collective intelligence. Especially by combining the collective intelligence with sensing control, we will drastically reduce the hectic tasks to collect and teach huge size of GT (ground truth) which has been serious bottleneck of conventional machine learning. Also it will greatly improve the performance of environment understanding beyond perception. In this article, we focus on the collective intelligence and investigate the technical realization regarding the evolutionary process of ET (estimated truth) towards GT.
{"title":"A proposal for Neuro-ITS over the connected vehicles network","authors":"M. Sasaki","doi":"10.1109/IVS.2015.7225653","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225653","url":null,"abstract":"On the basis of the recent technical trend of automated vehicles and connected vehicles, we have been proposing the new application concept Neuro-ITS which has three major technical features such as multi-viewpoint tracking, human appearance prediction, and collective intelligence. Especially by combining the collective intelligence with sensing control, we will drastically reduce the hectic tasks to collect and teach huge size of GT (ground truth) which has been serious bottleneck of conventional machine learning. Also it will greatly improve the performance of environment understanding beyond perception. In this article, we focus on the collective intelligence and investigate the technical realization regarding the evolutionary process of ET (estimated truth) towards GT.","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":"126333747","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.2015.7225656
G. Markkula, Johan Eklov, L. Laine, Erik Wikenhed, Niklas Frojd
An experiment was carried out on a low friction test track, where seven truck drivers repeatedly performed collision avoidance and stabilization with a 4×2 tractor. A previous finding from a simulator study was confirmed: In severe yaw instability, drivers engaged in a yaw rate nulling type of steering behavior, in conflict with the assumptions of conventional electronic stability control (ESC), and the experiment provided indications of conventional ESC behaving suboptimally in these situations. Promising results were obtained for modified versions of the ESC, based on the yaw rate nulling model of steering, but further development work is needed.
{"title":"Improving yaw stability control in severe instabilities by means of a validated model of driver steering","authors":"G. Markkula, Johan Eklov, L. Laine, Erik Wikenhed, Niklas Frojd","doi":"10.1109/IVS.2015.7225656","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225656","url":null,"abstract":"An experiment was carried out on a low friction test track, where seven truck drivers repeatedly performed collision avoidance and stabilization with a 4×2 tractor. A previous finding from a simulator study was confirmed: In severe yaw instability, drivers engaged in a yaw rate nulling type of steering behavior, in conflict with the assumptions of conventional electronic stability control (ESC), and the experiment provided indications of conventional ESC behaving suboptimally in these situations. Promising results were obtained for modified versions of the ESC, based on the yaw rate nulling model of steering, but further development work is needed.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"59 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":"126704855","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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