{"title":"Autonomous driving for vehicular networks with nonlinear dynamics","authors":"Lamia Iftekhar, R. Olfati-Saber","doi":"10.1109/IVS.2012.6232275","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232275","url":null,"abstract":"In this paper, we introduce cooperative autonomous driving algorithms for vehicular networks with nonlinear mobile robot dynamics in urban environments that take human safety into account and are capable of performing vehicle-to-vehicle (V2V) and vehicle-to-pedestrian (V2P) collision avoidance. We argue that “flocks” are multi-agent models of vehicular traffic on roads and propose novel autonomous driving architectures and algorithms for cyber-physical vehicles capable of performing autonomous driving tasks such as lane-driving, lane-changing, braking, passing, and making turns. Our proposed autonomous driving algorithms are inspired by Olfati-Saber's flocking theory. Though, there are notable differences between autonomous driving on urban roads and flocking behavior - flocks have a single desired destination whereas most drivers on road do not share the same destination. We refer to this collective behavior (driving) as “multi-objective flocking.” The self-driving vehicles in our framework turn out to be hybrid systems with a finite number of discrete states that are related to the driving modes of vehicles. Complex driving maneuvers can be performed using a sequence of mode switchings. We use near-identity nonlinear transformations to extend the application of particle-based autonomous driving algorithms to multi-robot networks with nonlinear dynamics. The derivation of the mode switching conditions that preserve safety is non-trivial and an important part of the design of autonomous driving algorithms. We present several examples of driving tasks that can be effectively performed using our proposed driving algorithms.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129347009","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}
{"title":"A comparison of two different tracking algorithms is provided for real application","authors":"L. Lamard, R. Chapuis, Jean-Philippe Boyer","doi":"10.1109/IVS.2012.6232173","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232173","url":null,"abstract":"The Multiple Hypothesis Tracker (MHT) and the Cardinalized Probability Hypothesis Density (CPHD) are two algorithms which can overcome the Multi-Targets Tracking (MTT) issues in automotive applications. This paper describes the performance of such algorithms and, in particular the Gaussian Mixture Probability Hypothesis Density (GMPHD) filter and the Track Oriented Multiple Hypothesis Tracker (TOMHT) for multiple cars and humans tracking in real road context. The scenario under consideration is the tracking an unknown number of real targets (humans and vehicles), using real measurements from an intelligent camera and a radar. The estimation of the number of targets and the target states of each filter will allow us to draw conclusion regarding the behavior of TOMHT and GMCPHD in real road context.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129533176","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}
{"title":"Cooperative driving in mixed traffic networks — Optimizing for performance","authors":"S. Calvert, T. V. D. Broek, M. Noort","doi":"10.1109/IVS.2012.6232138","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232138","url":null,"abstract":"This paper discusses a cooperative adaptive cruise control application and its effects on the traffic system. In previous work this application has been tested on the road, and traffic simulation has been used to scale up the results of the field test to larger networks and more vehicles. The present study investigates the dependence of the traffic impact of the time headway settings of the application and on its penetration rate. It will be shown both theoretically and empirically that traffic flows and road capacities will improve significantly with the fraction of equipped vehicles, and that this improvement depends on the configuration of the application.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129299585","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}
Feihu Zhang, Guang Chen, H. Stahle, C. Buckl, A. Knoll
{"title":"Visual odometry based on Random Finite Set Statistics in urban environment","authors":"Feihu Zhang, Guang Chen, H. Stahle, C. Buckl, A. Knoll","doi":"10.1109/IVS.2012.6232201","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232201","url":null,"abstract":"This paper presents a novel approach for estimating the vehicle's trajectory in complex urban environments. In previous work, we presented a visual odometry solution that estimates frame-to-frame motion from a single camera based on Random Finite Set (RFS) Statistics. This paper extends that work by combining the stereo cameras and gyroscope sensor. We are among the first to apply RFS statistics to visual odometry in real traffic scenes. The method is based on two phases: a preprocessing phase to extract features from the image and transform the coordinates from the image space to vehicle coordinates; a tracking phase to estimate the egomotion vector of the camera. We consider features as a group target and use the Probability Hypothesis Density (PHD) filter to update the overall group state as the motion vector. Compared to other approaches, our method presents a recursive filtering algorithm that provides dynamic estimation of multiple-targets states in the presence of clutter and high association uncertainty. The experimental results show that this method exhibits good robustness under various scenarios.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130674173","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}
Marcus Obst, S. Bauer, Pierre Reisdorf, G. Wanielik
{"title":"Multipath detection with 3D digital maps for robust multi-constellation GNSS/INS vehicle localization in urban areas","authors":"Marcus Obst, S. Bauer, Pierre Reisdorf, G. Wanielik","doi":"10.1109/IVS.2012.6232285","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232285","url":null,"abstract":"Reliable knowledge of the ego position for vehicles is a crucial requirement for many automotive applications. In order to solve this problem for satellite-based localization in dense urban areas, multipath situations need to be handled carefully. This paper proposes a lightweight multipath detection algorithm which is based on dynamically built 3D environmental maps. The algorithm is evaluated with simulated and real-world data. Furthermore, it is applied to a combined GPS and GLONASS system in combination with a loosely coupled integration of odometry measurements from the vehicle.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131383280","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}
{"title":"Electric vehicle travel optimization-customer satisfaction despite resource constraints","authors":"N. Hoch, Kevin Zemmer, B. Werther, R. Siegwart","doi":"10.1109/IVS.2012.6232240","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232240","url":null,"abstract":"Consumers and producers of mobility products have been co-creating a mobile world - all within the limits of political regulation and infrastructure realities. With the advent of electric vehicles, the existing mobile world requires adaptation: Producers need to create new ecosystems and mobility concepts ([1], [2]), infrastructure requires adaptation ([1]) and lastly consumers might revisit their expectations. Particularly challenging is the market introduction phase of electric vehicles. Neither have the potentials of the infrastructure and the electric vehicles been fully exploited, nor have consumers become accustomed to electric vehicles and shaped their expectations accordingly. Making electric vehicles a success story requires the satisfaction of customer expectations in the face of both electric vehicle and infrastructure realities. This paper suggests an optimization approach which maximizes customer satisfaction for existing electric vehicle and infrastructure realities. For various degrees-of-freedom (DoF) of the mobility system, the improvement potential is analysed with respect to consumption, charging time, cost and travel time. Moreover, the optimization complexity is analysed, which scales with the number of DoF. The approach enables market entry of electric vehicles and provides the means for future e-navigation and e-travel-planning.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131964704","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}
{"title":"On-road position estimation by probabilistic integration of visual cues","authors":"V. Popescu, R. Danescu, S. Nedevschi","doi":"10.1109/IVS.2012.6232182","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232182","url":null,"abstract":"This paper addresses the problem of finding the host vehicle's lateral position on a multi-lane road, using information obtained by processing video sequences. A very important cue for lane identification is the class of the boundaries of the current lane. This paper presents a reliable solution for lane boundary type identification, based on frequency analysis of the gray level profile of these boundaries, assuming that the current lane is already detected. The lane boundary information is combined with the obstacle information, through a Bayesian Network which will output, frame by frame, the probability of the vehicle to be positioned on each lane of the road. The probability result will be propagated throughout the sequence by a Particle Filter.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134314449","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}
{"title":"Cooperative driving: Beyond V2V as an ADAS sensor","authors":"D. Caveney, W. Dunbar","doi":"10.1109/IVS.2012.6232210","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232210","url":null,"abstract":"Vehicle-to-Vehicle (V2V) communication systems utilize wireless communications for shared sensing between vehicles. This paper discusses how V2V systems could be utilized, beyond shared sensing, for shared decision making between cooperative vehicles. We propose distributed receding horizon control (DRHC) as an appropriate mechanism for scalable, shared decision making. Two automated driving applications, platooning and cooperative merging, illustrate the use of essential enabling technologies, including geo-spatial positions, digital road maps, collision avoidance, and path prediction, and how each is incorporated through our DRHC-centric framework. At the core of the framework is a four-task logic that allows partially-synchronous execution of local, computationally-efficient, optimization problems on board each vehicle.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131140512","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}
P. George, I. Thouvenin, V. Fremont, V. Berge-Cherfaoui
{"title":"DAARIA: Driver assistance by augmented reality for intelligent automobile","authors":"P. George, I. Thouvenin, V. Fremont, V. Berge-Cherfaoui","doi":"10.1109/IVS.2012.6232220","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232220","url":null,"abstract":"Taking into account the drivers' state is a major challenge for designing new advanced driver assistance systems. In this paper we present a driver assistance system strongly coupled to the user. DAARIA1 stands for Driver Assistance by Augmented Reality for Intelligent Automobile. It is an augmented reality interface powered by several sensors. The detection has two goals: one is the position of obstacles and the quantification of the danger represented by them. The other is the driver's behavior. A suitable visualization metaphor allows the driver to perceive at any time the location of the relevant hazards while keeping his eyes on the road. First results show that our method could be applied to a vehicle but also to aerospace, fluvial or maritime navigation.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133079521","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}
{"title":"Generation of high precision digital maps using circular arc splines","authors":"A. Schindler, G. Maier, F. Janda","doi":"10.1109/IVS.2012.6232124","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232124","url":null,"abstract":"Digital maps can provide essential information for many advanced driver assistance systems (ADAS) dedicated to both safety and comfort applications. As the level of detail and global accuracy of state-of-the-art digital maps are not sufficient for a multitude of applications, we present methods and models for the generation of high precision maps. The proposed modeling includes 3D lane level information, road markings, landmarks and additional attributes with benefits for many ADAS. The extensive use of circular arc splines enables both adjustable accuracy and high efficiency as our cartographic methodology guarantees the minimum number of curve segments with respect to a given error threshold.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132170156","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}