Pub Date : 2015-08-27DOI: 10.1109/IVS.2015.7225773
Michael Henzler, M. Buchholz, K. Dietmayer
This paper presents an improved approach to the problem of energy efficient driving of heavy duty vehicles. The proposed model for a map-based Model Predictive Control (MPC) leads to an underlying Quadratic Programming (QP) optimization problem, allowing computationally efficient and robust solutions. A parameter estimation procedure is developed for a vehicle- and optimization-independent parametrization of the tradeoff between saving energy and keeping a desired vehicle velocity. Extensive simulations on a highway scenario for different optimization parameters give further insight to optimization properties, which can be utilized to enhance control performance. Compared to previous literature, we demonstrate a significant improvement of the computation time to under one-fifth of a millisecond, while maintaining (or even increasing) the fuel consumption reduction, which is 8.1 percent with the proposed approach compared to a standard cruise controller, without a decrease in the average cruising speed.
{"title":"Optimal parameter selection of a Model Predictive Control algorithm for energy efficient driving of heavy duty vehicles","authors":"Michael Henzler, M. Buchholz, K. Dietmayer","doi":"10.1109/IVS.2015.7225773","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225773","url":null,"abstract":"This paper presents an improved approach to the problem of energy efficient driving of heavy duty vehicles. The proposed model for a map-based Model Predictive Control (MPC) leads to an underlying Quadratic Programming (QP) optimization problem, allowing computationally efficient and robust solutions. A parameter estimation procedure is developed for a vehicle- and optimization-independent parametrization of the tradeoff between saving energy and keeping a desired vehicle velocity. Extensive simulations on a highway scenario for different optimization parameters give further insight to optimization properties, which can be utilized to enhance control performance. Compared to previous literature, we demonstrate a significant improvement of the computation time to under one-fifth of a millisecond, while maintaining (or even increasing) the fuel consumption reduction, which is 8.1 percent with the proposed approach compared to a standard cruise controller, without a decrease in the average cruising speed.","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":"114194602","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.2015.7225905
Haiyue Piao, Yongtae Park, Byungjo Kim, Hyogon Kim
In the IEEE Wireless Access in Vehicular Environment (WAVE) environment, periodic beacon messages enable proximity awareness that helps prevent collisions. One of the barriers that hinder the beacon delivery is congestion, which means that the amount of beacon traffic exceeds the Dedicated Short Range Communication (DSRC) channel capacity. In this paper, we propose a scheme for vehicles to independently adjust the beaconing rate based on the estimated neighbor vehicle population. Unlike previously proposed works, the proposed scheme does not need cross-layer information such as channel busy ratio (CBR) in order to achieve significant beacon throughput. We demonstrate through extensive simulations that this scheme significantly improves the beacon throughput.
{"title":"Safety beaconing rate control based on vehicle counting in WAVE","authors":"Haiyue Piao, Yongtae Park, Byungjo Kim, Hyogon Kim","doi":"10.1109/IVS.2015.7225905","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225905","url":null,"abstract":"In the IEEE Wireless Access in Vehicular Environment (WAVE) environment, periodic beacon messages enable proximity awareness that helps prevent collisions. One of the barriers that hinder the beacon delivery is congestion, which means that the amount of beacon traffic exceeds the Dedicated Short Range Communication (DSRC) channel capacity. In this paper, we propose a scheme for vehicles to independently adjust the beaconing rate based on the estimated neighbor vehicle population. Unlike previously proposed works, the proposed scheme does not need cross-layer information such as channel busy ratio (CBR) in order to achieve significant beacon throughput. We demonstrate through extensive simulations that this scheme significantly improves the beacon throughput.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"17 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":"116092839","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.2015.7225858
Fei Yan, Lars Weber, A. Lüdtke
Driver's uncertainty in lane change situations could cause longer reaction times and even lead to wrong decisions, which is very dangerous for the critical driving task. We assume that reducing driver's uncertainty with assistance systems in lane change situations can not only increase traffic safety, but also increase driver's trust in assistance systems. In order to develop trustworthy assistance systems, this paper starts from classifying driver's uncertainty about distance gaps and studies the impact of distance gaps on driver's uncertainty at lane changing. In the driving simulator experiment, participants were asked to take steering or brake actions in different lane change situations. Their reaction times to an acoustic signal ordering to start changing lanes and subjective certainty scores were collected and analyzed. The results showed that with the constant closing speed of 10 km/h between the ego vehicle and rear vehicle, the brake action was more often preferred than the steering and participants were relatively certain with short reaction times at small distance gaps (<; 32m). At large distance gaps (> 44m), the steering was more often chosen than brake actions and participants were also certain with short reaction times. However, when the distance gap was in between (32m, 36m, 40m, 44m), participants were very uncertain and had relatively long reaction times.
{"title":"Classifying driver's uncertainty about the distance gap at lane changing for developing trustworthy assistance systems","authors":"Fei Yan, Lars Weber, A. Lüdtke","doi":"10.1109/IVS.2015.7225858","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225858","url":null,"abstract":"Driver's uncertainty in lane change situations could cause longer reaction times and even lead to wrong decisions, which is very dangerous for the critical driving task. We assume that reducing driver's uncertainty with assistance systems in lane change situations can not only increase traffic safety, but also increase driver's trust in assistance systems. In order to develop trustworthy assistance systems, this paper starts from classifying driver's uncertainty about distance gaps and studies the impact of distance gaps on driver's uncertainty at lane changing. In the driving simulator experiment, participants were asked to take steering or brake actions in different lane change situations. Their reaction times to an acoustic signal ordering to start changing lanes and subjective certainty scores were collected and analyzed. The results showed that with the constant closing speed of 10 km/h between the ego vehicle and rear vehicle, the brake action was more often preferred than the steering and participants were relatively certain with short reaction times at small distance gaps (<; 32m). At large distance gaps (> 44m), the steering was more often chosen than brake actions and participants were also certain with short reaction times. However, when the distance gap was in between (32m, 36m, 40m, 44m), participants were very uncertain and had relatively long reaction times.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"45 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121014204","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.2015.7225701
David Lenz, Tobias Kessler, A. Knoll
In this paper, we address the application of stochastic model predictive control with chance constraints to autonomous driving. We use a condensed formulation of a linearized vehicle model to setup a quadratic program with nonlinear chance constraints, which can be solved with off-the-shelf optimization algorithms. We further show how obstacle information in the path planning stage can be converted into a set of linear state constraints that can be directly used in the control algorithm. The resulting controller is potentially real-time capable and achieves a tradeoff between safety and comfort in its control behavior.
{"title":"Stochastic model predictive controller with chance constraints for comfortable and safe driving behavior of autonomous vehicles","authors":"David Lenz, Tobias Kessler, A. Knoll","doi":"10.1109/IVS.2015.7225701","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225701","url":null,"abstract":"In this paper, we address the application of stochastic model predictive control with chance constraints to autonomous driving. We use a condensed formulation of a linearized vehicle model to setup a quadratic program with nonlinear chance constraints, which can be solved with off-the-shelf optimization algorithms. We further show how obstacle information in the path planning stage can be converted into a set of linear state constraints that can be directly used in the control algorithm. The resulting controller is potentially real-time capable and achieves a tradeoff between safety and comfort in its control behavior.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"11 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":"125290558","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.2015.7225663
Sung Gu Yi, C. Kang, Seung-Hi Lee, C. Chung
In this paper, we propose a new vehicle trajectory prediction algorithm for adaptive cruise control (ACC). When vehicle trajectory prediction is not precise enough, it is possible for a neighboring vehicle to be detected as a target. Thus, we propose a new method using both yaw rate and curvature rate to precisely predict vehicle trajectory and to resolve an undesirable case in ACC system. The proposed method was validated via CarSim and MATLAB/Simulink. Also, we validated the proposed method via experimental results with a test vehicle on highway system for the practicality.
{"title":"Vehicle trajectory prediction for adaptive cruise control","authors":"Sung Gu Yi, C. Kang, Seung-Hi Lee, C. Chung","doi":"10.1109/IVS.2015.7225663","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225663","url":null,"abstract":"In this paper, we propose a new vehicle trajectory prediction algorithm for adaptive cruise control (ACC). When vehicle trajectory prediction is not precise enough, it is possible for a neighboring vehicle to be detected as a target. Thus, we propose a new method using both yaw rate and curvature rate to precisely predict vehicle trajectory and to resolve an undesirable case in ACC system. The proposed method was validated via CarSim and MATLAB/Simulink. Also, we validated the proposed method via experimental results with a test vehicle on highway system for the practicality.","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":"130426646","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.2015.7225659
Zhilu Chen, Quan Shi, Xinming Huang
Many traffic accidents occurred at intersections are caused by drivers who miss or ignore the traffic signals. In this paper, we present a new method for automatic detection of traffic lights that integrates both image processing and support vector machine techniques. An experimental dataset with 21299 samples is built from the captured original videos while driving on the streets. When compared to the traditional object detection and existing methods, the proposed system provides significantly better performance with 96.97% precision and 99.43% recall. The system framework is extensible that users can introduce additional parameters to further improve the detection performance.
{"title":"Automatic detection of traffic lights using support vector machine","authors":"Zhilu Chen, Quan Shi, Xinming Huang","doi":"10.1109/IVS.2015.7225659","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225659","url":null,"abstract":"Many traffic accidents occurred at intersections are caused by drivers who miss or ignore the traffic signals. In this paper, we present a new method for automatic detection of traffic lights that integrates both image processing and support vector machine techniques. An experimental dataset with 21299 samples is built from the captured original videos while driving on the streets. When compared to the traditional object detection and existing methods, the proposed system provides significantly better performance with 96.97% precision and 99.43% recall. The system framework is extensible that users can introduce additional parameters to further improve the detection performance.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"301 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":"134261886","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.2015.7225678
Kuo-Yun Liang, Q. Deng, J. Mårtensson, Xiaoliang Ma, K. Johansson
Heavy-duty vehicle (HDV) platooning is a mean to significantly reduce the fuel consumption for the trailing vehicle. By driving close to the vehicle in front, the air drag is reduced tremendously. Due to each HDV being assigned with different transport missions, platoons will need to be frequently formed, merged, and split. Driving on the road requires interaction with surrounding traffic and road users, which will influence how well a platoon can be formed. In this paper, we study how traffic may affect a merging maneuver of two HDVs trying to form a platoon. We simulate this for different traffic densities and for different HDV speeds. Even on moderate traffic density, a platoon merge could be delayed with 20 % compared to the ideal case with no traffic.
{"title":"The influence of traffic on heavy-duty vehicle platoon formation","authors":"Kuo-Yun Liang, Q. Deng, J. Mårtensson, Xiaoliang Ma, K. Johansson","doi":"10.1109/IVS.2015.7225678","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225678","url":null,"abstract":"Heavy-duty vehicle (HDV) platooning is a mean to significantly reduce the fuel consumption for the trailing vehicle. By driving close to the vehicle in front, the air drag is reduced tremendously. Due to each HDV being assigned with different transport missions, platoons will need to be frequently formed, merged, and split. Driving on the road requires interaction with surrounding traffic and road users, which will influence how well a platoon can be formed. In this paper, we study how traffic may affect a merging maneuver of two HDVs trying to form a platoon. We simulate this for different traffic densities and for different HDV speeds. Even on moderate traffic density, a platoon merge could be delayed with 20 % compared to the ideal case with no traffic.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"67 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":"132160829","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.2015.7225743
S. Park, Beomjun Kim, Kyuwon Kim, Youngseop Son, K. Yi
This paper presents a time delay compensation algorithm for environmental sensors of automated driving systems. The time delay involved with the transmission of the measurements from the sensors to the processor cannot be negligible because it is responsible for estimation and control of the system. As the automotive environmental sensors such as laser scanner or radar perform measurements at a constant frequency, the measurement time latencies can be assumed to be constant. From this aspect, the constant time delay characteristics is analyzed via vehicle tests and compensated by forward estimation based coordinate transformation. The proposed compensation algorithm has been verified via test data based open loop simulation of Automated Driving Systems (ADS). It is shown that the proposed compensation enhances environment perception performance and driver's safety.
{"title":"Time delay compensation for environmental sensors of high-level automated driving systems","authors":"S. Park, Beomjun Kim, Kyuwon Kim, Youngseop Son, K. Yi","doi":"10.1109/IVS.2015.7225743","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225743","url":null,"abstract":"This paper presents a time delay compensation algorithm for environmental sensors of automated driving systems. The time delay involved with the transmission of the measurements from the sensors to the processor cannot be negligible because it is responsible for estimation and control of the system. As the automotive environmental sensors such as laser scanner or radar perform measurements at a constant frequency, the measurement time latencies can be assumed to be constant. From this aspect, the constant time delay characteristics is analyzed via vehicle tests and compensated by forward estimation based coordinate transformation. The proposed compensation algorithm has been verified via test data based open loop simulation of Automated Driving Systems (ADS). It is shown that the proposed compensation enhances environment perception performance and driver's safety.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"7 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":"128313527","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.2015.7225735
Andreas Löcken, Heiko Müller, Wilko Heuten, Susanne CJ Boll
In the recent years, several automotive manufacturers started to integrate ambient light displays into cars to increase drivers' comfort. Expanding their possible application areas, we propose a display that continuously informs the driver of the vehicle's as well as the environment's state. We studied this display in a lane change maneuver, in which a driver has to decide if he or she can change lane in front of a faster closing car or brake to keep a safe distance to a slower car in front. We present results of an experiment for light patterns that are based on results of a design workshop and definitions for lane change decision aid systems (LCDAS) of ISO 17387. Though we used ISO's definitions for the timings, our participants felt that status updates on the display came too late. In addition, the abrupt warnings, implemented in one of the tested patterns, led to worse performance of the participants. On the other hand, we observed that participants liked a continuous encoding of the time-to-collision (TTC) and observed a decrease in missed opportunities to overtake. Therefore, we argue that the defined limits for the warning levels are not well suited to support drivers during decision making in our scenario. Our contribution lies in a novel way of supporting drivers during lane change using an ambient in-vehicle light display. We showed that a continuous light pattern might help drivers in decision making, while more research has to be done to validate this.
{"title":"An experiment on ambient light patterns to support lane change decisions","authors":"Andreas Löcken, Heiko Müller, Wilko Heuten, Susanne CJ Boll","doi":"10.1109/IVS.2015.7225735","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225735","url":null,"abstract":"In the recent years, several automotive manufacturers started to integrate ambient light displays into cars to increase drivers' comfort. Expanding their possible application areas, we propose a display that continuously informs the driver of the vehicle's as well as the environment's state. We studied this display in a lane change maneuver, in which a driver has to decide if he or she can change lane in front of a faster closing car or brake to keep a safe distance to a slower car in front. We present results of an experiment for light patterns that are based on results of a design workshop and definitions for lane change decision aid systems (LCDAS) of ISO 17387. Though we used ISO's definitions for the timings, our participants felt that status updates on the display came too late. In addition, the abrupt warnings, implemented in one of the tested patterns, led to worse performance of the participants. On the other hand, we observed that participants liked a continuous encoding of the time-to-collision (TTC) and observed a decrease in missed opportunities to overtake. Therefore, we argue that the defined limits for the warning levels are not well suited to support drivers during decision making in our scenario. Our contribution lies in a novel way of supporting drivers during lane change using an ambient in-vehicle light display. We showed that a continuous light pattern might help drivers in decision making, while more research has to be done to validate this.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"199 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":"128517788","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.2015.7225789
M. Barjenbruch, Dominik Kellner, J. Klappstein, J. Dickmann, K. Dietmayer
An ego-motion estimation method based on the spatial and Doppler information obtained by an automotive radar is proposed. The estimation of the motion state vector is performed in a density-based framework. Compared to standard vehicle odometry the approach is capable to estimate the full two dimensional motion state with three degrees of freedom. The measurement of a Doppler radar sensor is represented as a mixture of Gaussians. This mixture is matched with the mixture of a previous measurement by applying the appropriate egomotion transformation. The parameters of the transformation are found by the optimization of a suitable join metric. Due to the Doppler information the method is very robust against disturbances by moving objects and clutter. It provides excellent results for highly nonlinear movements. Real world results of the proposed method are presented. The measurements are obtained by a 77GHz radar sensor mounted on a test vehicle. A comparison using a high-precision inertial measurement unit with differential GPS support is made. The results show a high accuracy in velocity and yaw-rate estimation.
{"title":"Joint spatial- and Doppler-based ego-motion estimation for automotive radars","authors":"M. Barjenbruch, Dominik Kellner, J. Klappstein, J. Dickmann, K. Dietmayer","doi":"10.1109/IVS.2015.7225789","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225789","url":null,"abstract":"An ego-motion estimation method based on the spatial and Doppler information obtained by an automotive radar is proposed. The estimation of the motion state vector is performed in a density-based framework. Compared to standard vehicle odometry the approach is capable to estimate the full two dimensional motion state with three degrees of freedom. The measurement of a Doppler radar sensor is represented as a mixture of Gaussians. This mixture is matched with the mixture of a previous measurement by applying the appropriate egomotion transformation. The parameters of the transformation are found by the optimization of a suitable join metric. Due to the Doppler information the method is very robust against disturbances by moving objects and clutter. It provides excellent results for highly nonlinear movements. Real world results of the proposed method are presented. The measurements are obtained by a 77GHz radar sensor mounted on a test vehicle. A comparison using a high-precision inertial measurement unit with differential GPS support is made. The results show a high accuracy in velocity and yaw-rate estimation.","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":"129381838","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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