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会议信息

会议名称:

IEEE Intelligent Vehicles Symposium

召开年:

2017

召开地:

Los Angeles

会议文集:

2017 IEEE Intelligent Vehicles Symposium: IV 2017, Los Angeles, California, USA, 11-14 June 2017, pages 1-617

主办单位:

Institute of Electrical and Electronics Engineers

出版时间:

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  • 题名 作者 来源 发表时间 操作
  • Distributed robust vehicle state estimation

    作者:Ehsan Hashemi;Mohammad Pirani;Baris Fidan;Amir Khajepour;Shih-ken Chen;Bakhtiar Litkouhi; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    A distributed estimation approach based on opinion dynamics is proposed to enhance the reliability of vehicle corners' velocity estimates. The corners' estimates, which are obtained from a Kalman filter, is formed by integrating the model-based and kinematic-based velocity estimation approaches. These estimates are utilized as opinions with different levels of confidence in the developed algorithm. More reliable estimates robust to disturbances and time delay are achieved via solving a convex optimization problem. Vehicle tests with various driveline configurations are performed to verify the estimator performance under different surfaces friction conditions in pure and combined-slip (combination of longitudinal/lateral) maneuvers, which are arduous for the current vehicle state estimators.

    关键字:Tires;Vehicle dynamics;Reliability;Friction;State estimation;Kalman filters

  • Distributed robust vehicle state estimation

    Ehsan Hashemi;Mohammad Pirani;Baris Fidan;Amir Khajepour;Shih-ken Chen;Bakhtiar Litkouhi;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Domain-specific data augmentation for on-road object detection based on a deep neural network

    作者:Hui Eun Kim;Youngwan Lee;Hakil Kim;Xuenan Cui; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    This paper proposes a data augmentation strategy for improving on-road object detection based on a deep neural network. The method uses a single camera and detects objects based on an optimized deep neural network for a driving environment. The strategy also uses a single-shot multi-box detector (SSD) for object detection, which is a state-of-the-art deep-learning algorithm. The performance is improved by using data augmentation for an advanced driver assist system (ADAS) specific to on-road object recognition. The problem of object detection is first analyzed based on a deep neural network in the ADAS domain, and then representative object detection methods that use deep neural networks are surveyed. A restricted random crop process is suggested for detecting small objects in an image, and then a patch resampling strategy is proposed for solving the long tail property in an on-road dataset. The proposed ADAS domain-specific data augmentation method is adjusted for the original object detection method based on a deep neural network. The object detection results were evaluated using an embedded board on the KITTI benchmark dataset, and the suggested data augmentation method improves the average precision by 30\%.

    关键字:Object detection;Training;Machine learning;Detectors;Neural networks;Feature extraction;Roads

  • Domain-specific data augmentation for on-road object detection based on a deep neural network

    Hui Eun Kim;Youngwan Lee;Hakil Kim;Xuenan Cui;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Assessing driver cortical activity under varying levels of automation with functional near infrared spectroscopy

    作者:Srinath Sibi;Stephanie Baiters;Brian Mok;Martin Steiner;Wendy Ju; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    Information about drivers' mental states can be vital to the design of interfaces for highly automated vehicles. Functional near infrared spectroscopy (fNIRS) is a neuroimaging tool that is fast becoming popular to study the cortical activity of participants in HCI experiments and driving simulator studies in particular. The analysis methods of the fNIRS data create requirements in the experimental design such as repeated measures. In this paper, we present a study of the event related cortical activity of the drivers of manual, partially autonomous, and fully autonomous cars when performing lane changes using functional near infrared spectroscopic measures. We also present the experimental methodology that was adopted to meet the needs of the fNIRS measurement and the subsequent analysis. The study (N=28) was conducted in a driving simulator. Participants drove for approximately 7 minutes and performed 8 lane change maneuvers in each mode of automation. Multiple streams of data including 4 time-synced video recordings, NASA TLX questionnaires and fNIRS data were recorded and analyzed. It was found that the dorsolateral prefrontal cortex activation during lane changes performed in a partially autonomous mode of operation was just as high as that during a manual lane change, showing that drivers of partially automated systems are as cognitively engaged as drivers of manually operated vehicles.

    关键字:Vehicles;Manuals;Roads;Atmospheric measurements;Particle measurements;Monitoring;Automation

  • Assessing driver cortical activity under varying levels of automation with functional near infrared spectroscopy

    Srinath Sibi;Stephanie Baiters;Brian Mok;Martin Steiner;Wendy Ju;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Convoy tracking for ADAS on embedded GPUs

    作者:Jörg Fickenscher;Sebastian Reinhart;Frank Hannig;Jürgen Teich;Mohamed Essayed Bouzouraa; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    Future Advanced Driver Assistance Systems (ADAS) need to create an accurate model of the environment. Accordingly, an enormous amount of data has to be fused and processed. From this data, information such as the positions of the vehicles, has to be extracted out of the model, e.g., to create a convoy track. Common architectures used today, like single-core processors in automotive Electronic Control Units (ECUs), struggle to provide enough computing power for those tasks. Here, emerging embedded multi-core architectures are appealing such as embedded Graphics Processing Units (GPUs). In this paper, we present a novel parallelization of a convoy track detection algorithm. Moreover, in order to profit best from for embedded GPUs, special techniques such as Zero Copy are exploited to parallelize our application. As an experimental platform, an Nvidia Tegra K1 is used, which is also common in the automotive industry. For different scenarios, we illustrate the limitations of the system and algorithm. Yet, impressive speedups with respect to a single-core CPU solution of up to nine may be achieved using the proposed parallelization techniques in case of high traffic situations.

    关键字:Graphics processing units;Instruction sets;Central Processing Unit;Radar tracking;Sensors;Computational modeling;Computer architecture

  • Convoy tracking for ADAS on embedded GPUs

    Jörg Fickenscher;Sebastian Reinhart;Frank Hannig;Jürgen Teich;Mohamed Essayed Bouzouraa;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Driver-automation indirect shared control of highly automated vehicles with intention-aware authority transition

    作者:Renjie Li;Yanan Li;Shengbo Eben Li;Etienne Burdet;Bo Cheng; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    Shared control is an important approach to avoid the driver-out-of-the-loop problems brought by imperfect autonomous driving. Steer-by-wire technology allows the mechanical decoupling between the steering wheel and the road wheels. On steer-by-wire vehicles, the automation can join the control loop by correcting the driver steering input, which forms a new paradigm of shared control. The new framework, under which the driver indirectly controls the vehicle through the automation's input transformation, is called indirect shared control. This paper presents an indirect shared control system, which realizes the dynamic control authority allocation with respect to the driver's authority intention. The simulation results demonstrate the effectiveness and benefits of the proposed control authority adaptation method.

    关键字:Vehicles;Automation;Vehicle dynamics;Resource management;Wheels;Control systems;Adaptation models

  • Driver-automation indirect shared control of highly automated vehicles with intention-aware authority transition

    Renjie Li;Yanan Li;Shengbo Eben Li;Etienne Burdet;Bo Cheng;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Cooperative driving using a hierarchy of mixed-integer programming and tracking control

    作者:Jan Eilbrecht;Olaf Stursberg; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    This paper aims at cooperatively resolving conflicts arising between several fully-autonomous, communicating vehicles in general on-road traffic scenarios. A hierarchical control scheme with two main units is proposed: A short-term planning unit determines obstacle-avoiding reference trajectories in a receding-horizon-fashion, based on solving mixed-integer quadratic programs (MIQP) for simplified vehicle dynamics. A novel cooperative conflict resolution scheme is proposed which orchestrates the plans of neighboring vehicles, trying to minimize a common cost function. Tracking controllers based on model predictive control (MPC) are then employed to track the references using a nonlinear vehicle model. The functionality of the proposed method is demonstrated in a simulation example.

    关键字:Planning;Trajectory;Computational modeling;Roads;Vehicle dynamics;Optimization;Tires

  • Cooperative driving using a hierarchy of mixed-integer programming and tracking control

    Jan Eilbrecht;Olaf Stursberg;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Driving speed profiles for autonomous vehicles

    作者:Anton Anastassov;Dongwook Jang;Gavril Giurgiu; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    We show that driving speed profiles with high spatial resolution can be constructed using large samples of GPS data collected from vehicle fleets, consumer navigation devices, and smart phones. Such historical speed profiles are expected to help adaptive cruise control systems or autonomous vehicles navigate in a manner consistent with the way people drive. Knowing in advance the speed profiles of a trip may help the development of more energy efficient systems.

    关键字:Probes;Roads;Global Positioning System;Autonomous vehicles;Sensors

  • Driving speed profiles for autonomous vehicles

    Anton Anastassov;Dongwook Jang;Gavril Giurgiu;

    IEEE Intelligent Vehicles Symposium

    2017年

  • DSRC-based end of queue warning system

    作者:Yang Liu;Wei-Bin Zhang;Zhong-Li Wang;Ching-Yao Chan; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    The rapid development of the Dedicated Short Range Communications (DSRC) has provide a good opportunity for solving the increasingly outstanding traffic safety problems. One noteworthy function enabled by DSRC is the end of queue (EOQ) collision warning system. However, the reliability of the system is easily affected by some easily ignorable factors like DSRC penetration rate, DSRC communication range and positioning accuracy. In this paper, an EOQ warning system is proposed on a highway environment. The system is established using only velocity-related information combined with real highway data, which produce a warning threshold. For the proposed warning mechanism, we also build an evaluation model considering relevant influential factors. By means of simulation validation under different road conditions, an effective EOQ warning system can be issued in time when related parameters meet corresponding requirements.

    关键字:Alarm systems;Roads;Traffic control;Reliability;Vehicles;Meteorology;Probability

  • DSRC-based end of queue warning system

    Yang Liu;Wei-Bin Zhang;Zhong-Li Wang;Ching-Yao Chan;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Efficient ConvNet for real-time semantic segmentation

    作者:Eduardo Romera;José M. Álvarez;Luis M. Bergasa;Roberto Arroyo; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    Semantic segmentation is a task that covers most of the perception needs of intelligent vehicles in an unified way. ConvNets excel at this task, as they can be trained end-to-end to accurately classify multiple object categories in an image at the pixel level. However, current approaches normally involve complex architectures that are expensive in terms of computational resources and are not feasible for ITS applications. In this paper, we propose a deep architecture that is able to run in real-time while providing accurate semantic segmentation. The core of our ConvNet is a novel layer that uses residual connections and factorized convolutions in order to remain highly efficient while still retaining remarkable performance. Our network is able to run at 83 FPS in a single Titan X, and at more than 7 FPS in a Jetson TX1 (embedded GPU). A comprehensive set of experiments demonstrates that our system, trained from scratch on the challenging Cityscapes dataset, achieves a classification performance that is among the state of the art, while being orders of magnitude faster to compute than other architectures that achieve top precision. This makes our model an ideal approach for scene understanding in intelligent vehicles applications.

    关键字:Computer architecture;Image segmentation;Semantics;Real-time systems;Decoding;Deconvolution;Training

  • Efficient ConvNet for real-time semantic segmentation

    Eduardo Romera;José M. Álvarez;Luis M. Bergasa;Roberto Arroyo;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Efficient tracking of closely spaced objects in depth data using sequential dirichlet process clustering

    作者:Michael Hoy;Justin Dauwels;Junsong Yuan; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    Many approaches for tracking objects in lidar data have been proposed in recent years. However, most practical real time systems assume that clean segmentation of lidar points into individual objects can be achieved. Unfortunately, efficient lidar segmentation approaches are prone to under-segmentation when objects are very close to each other; one solution is to introduce additional segmentation steps into the tracking process. In this paper we propose a new method to address this task with distance dependent Chinese Restaurant Processes (dd-CRP) equipped with a shape prior defining possible object shapes. By adding constraints to the segmentation model, we are able to further improve stability of segmentation and tracking. Experiments on real datasets show the advantage of this approach over a baseline object tracking pipeline.

    关键字:Shape;Laser radar;Object tracking;Clustering algorithms;Computer architecture;Probabilistic logic

  • Efficient tracking of closely spaced objects in depth data using sequential dirichlet process clustering

    Michael Hoy;Justin Dauwels;Junsong Yuan;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Efficient L-shape fitting for vehicle detection using laser scanners

    作者:Xiao Zhang;Wenda Xu;Chiyu Dong;John M. Dolan; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    The detection of surrounding vehicles is an essential task in autonomous driving, which has been drawing enormous attention recently. When using laser scanners, L-Shape fitting is a key step for model-based vehicle detection and tracking, which requires thorough investigation and comprehensive research. In this paper, we formulate the L-Shape fitting as an optimization problem. An efficient search based method is then proposed to find the optimal solution. Our method does not rely on laser scan sequence information and therefore supports convenient data fusion from multiple laser scanners; it is efficient and involves very few parameters for tuning; the approach is also flexible to suit various fitting demands with different fitting criteria. On-road experiments with production-grade laser scanners have demonstrated the effectiveness and robustness of our approach.

    关键字:Feature extraction;Shape;Laser radar;Three-dimensional displays;Clustering algorithms;Vehicle detection;Automobiles

  • Efficient L-shape fitting for vehicle detection using laser scanners

    Xiao Zhang;Wenda Xu;Chiyu Dong;John M. Dolan;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Elastic band based pedestrian collision avoidance using V2X communication

    作者:Sukru Yaren Gelbal;Sibel Arslan;Haoan Wang;Bilin Aksun-Guvenc;Levent Guvenc; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    This paper is on a pedestrian collision warning and avoidance system for road vehicles based on V2X communication. In cases where the presence and location of a pedestrian or group of pedestrians cannot be determined using line-of-sight sensors like camera, radar and lidar, signals from pedestrians' smartphone apps are used to detect and localize them relative to the road vehicle through the DSRC radio used for V2X communication. A hardware-in-the-loop setup using a validated automated driving vehicle model in the high fidelity vehicle dynamics simulation program Carsim Real Time with Sensors and Traffic is used along with two DSRC modems emulating the vehicle and pedestrian communications in the development and initial experimental testing of this method. The vehicle either stops or, if possible, goes around the pedestrians in a socially acceptable manner. The elastic band method is used to locally modify the vehicle trajectory in real time when pedestrians are detected on the nearby path of the vehicle. The effectiveness of the proposed method is demonstrated using hardware-in-the-loop simulations.

    关键字:Collision avoidance;Safety;Autonomous vehicles;Sensors;Modems;Force;Wireless communication

  • Elastic band based pedestrian collision avoidance using V2X communication

    Sukru Yaren Gelbal;Sibel Arslan;Haoan Wang;Bilin Aksun-Guvenc;Levent Guvenc;

    IEEE Intelligent Vehicles Symposium

    2017年

  • End-to-end learning for lane keeping of self-driving cars

    作者:Zhilu Chen;Xinming Huang; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    Lane keeping is an important feature for self-driving cars. This paper presents an end-to-end learning approach to obtain the proper steering angle to maintain the car in the lane. The convolutional neural network (CNN) model takes raw image frames as input and outputs the steering angles accordingly. The model is trained and evaluated using the comma.ai dataset, which contains the front view image frames and the steering angle data captured when driving on the road. Unlike the traditional approach that manually decomposes the autonomous driving problem into technical components such as lane detection, path planning and steering control, the end-to-end model can directly steer the vehicle from the front view camera data after training. It learns how to keep in lane from human driving data. Further discussion of this end-to-end approach and its limitation are also provided.

    关键字:Data models;Roads;Training;Computational modeling;Cameras;Autonomous automobiles;Computer architecture

  • End-to-end learning for lane keeping of self-driving cars

    Zhilu Chen;Xinming Huang;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Analysis of ITS-G5A V2X communications performance in autonomous cooperative driving experiments

    作者:Ignacio Parra;Alvaro García-Morcillo;Rubén Izquierdo;Javier Alonso;D. Fernández-Llorca;M.A. Sotelo; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    In this paper the performance of ITS-G5A communications for an autonomous driving application is analyzed in a real high-density scenario. The data was collected during the cooperative platooning tests that took place in Helmond in the frame of the Grand Cooperative Driving Challenge 2016. In the competition, between 8-10 autonomous vehicles formed two platoons in different lanes and were required to merge into a predefined competition zone. The performance is characterized using CAM CCDFs which serves as a base for the evaluation of a Cooperative Adaptive Cruise Control application. Two important effects has been identified that affect to the reliability of the communications. Firstly, there is a degradation with the distance that appears to be stronger for cars and more gentle for trucks. This indicates that occlusions heavily affect the connectivity of ITS-G5A. Secondly, the reliability is below expectations and some of the vehicles perform consistently worse than others. Although further investigation is required, a possible explanation for this is that a highly congested channel is making some of the vehicles get stuck and are not able to regularly access the channel.

    关键字:Standards;Delays;Reliability;Safety;Heating systems;Automobiles;Road transportation

  • Analysis of ITS-G5A V2X communications performance in autonomous cooperative driving experiments

    Ignacio Parra;Alvaro García-Morcillo;Rubén Izquierdo;Javier Alonso;D. Fernández-Llorca;M.A. Sotelo;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Design of the control strategy for a range extended hybrid vehicle by means of dynamic programming optimization

    作者:Claudio Cubito;Luciano Rolando;Alessandro Ferraris;Massimiliana Carello;Federico Millo; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    Electric vehicles (EVs) are attractive to reduce the pollution levels of urban areas thanks to their zero tail pipe emissions and to the possibility to rely on renewable energies for the electricity production. However, the market penetration of such vehicles is restricted by their limited range capabilities and by the lack of fast charging infrastructures. The Range Extended (R-EX) hybrid technology represents a valuable option to address these issues, since it offers the benefits of an EV, such as the electric driving for medium distances, while still maintaining the internal combustion engine, which can be operated at its optimal efficiency to recharge the battery, thus eliminating the driver's range anxiety. This article illustrates the design of the Energy Management System (EMS) of an R-EX Ultra-Light Vehicle (ULV), applying a heuristic approach based on the results obtained from the Dynamic Programming (DP) optimization, maximising the overall powertrain efficiency as function of trip information.

    关键字:Batteries;Energy management;Engines;Optimization;Mechanical power transmission;Fuels;Electronic mail

  • Design of the control strategy for a range extended hybrid vehicle by means of dynamic programming optimization

    Claudio Cubito;Luciano Rolando;Alessandro Ferraris;Massimiliana Carello;Federico Millo;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Developing a platoon-wide Eco-Cooperative Adaptive Cruise Control (CACC) system

    作者:Ziran Wang;Guoyuan Wu;Peng Hao;Kanok Boriboonsomsin;Matthew Barth; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    Connected and automated vehicle (CAV) technology has become increasingly popular. As an example, Cooperative Adaptive Cruise Control (CACC) systems are of high interest, allowing CAVs to communicate and cooperate with each other to form platoons, where one vehicle follows another with a predefined spacing or time gap. Although numerous studies have been conducted on CACC systems, very few have examined the protocols from the perspective of environmental sustainability, not to mention from a platoon-wide consideration. In this study, we propose a vehicle-to-vehicle (V2V) communication based Eco-CACC system, aiming to minimize the platoon-wide energy consumption and pollutant emissions at different stages of the CACC operation. A full spectrum of environmentally-friendly CACC maneuvers are explored and the associated protocols are developed, including sequence determination, gap closing and opening, platoon cruising with gap regulation, and platoon joining and splitting. Simulation studies of different scenarios are conducted using MATLAB/Simulink. Compared to an existing CACC system, the proposed one can achieve additional 2\% energy savings and additional 17\% pollutant emissions reductions during the platoon joining scenario.

    关键字:Protocols;Energy consumption;Cruise control;Electronic mail;Acceleration;Adaptive systems;Urban areas

  • Developing a platoon-wide Eco-Cooperative Adaptive Cruise Control (CACC) system

    Ziran Wang;Guoyuan Wu;Peng Hao;Kanok Boriboonsomsin;Matthew Barth;

    IEEE Intelligent Vehicles Symposium

    2017年

  • A two-stage electric vehicles scheduling strategy to address economic inconsistency issues of stakeholders

    作者:Bing Han;Shaofeng Lu;Fei Xue;Lin Jiang;Huaiying Zhu; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    As a promising mobility tool in future transportation systems, Electric Vehicle (EV) has environment-friendly benefits compared with traditional internal-combustion-engine vehicle. However, uncoordinated charging of mass EVs bring huge burden to power grids. To tackle this problem, a coordinated charging strategy of EVs is necessary. EV aggregator could play as a coordinator between EV owner and power grids, both meeting owner driving requirements and power grids operation requirements. However, a owner-aggregator economic inconsistency issue appears, that is EV owner get a higher charging cost in aggregator scheduling than self scheduling. In order to mediate owner-aggregator economic inconsistency issue, this paper designed a centralized two-stage EVs charging/discharging scheduling strategy in a residential community within 24 hours from the viewpoint of two stakeholders: EV owners (to minimize each EV owner charging cost) and aggregator (to maximize aggregator revenue). In the first-stage, EVs operation are scheduled from EV owners viewpoint, to obtain the minimal charging cost for each EV owner. Then, in the second-stage, the scheduling results in the first-stage are involved as constraints. The objective in the second-stage is to maximize aggregator revenue, without sacrificing each EV owner's economic benefit (no charging cost increment). A rebate factor is introduced in this model, which is the pay back for each EV owner provided by aggregator. Case study shows the effectiveness of the proposed scheduling strategy: the aggregator revenue is maximized without sacrificing each EV owner's economic benefit so that owner-aggregator economic inconsistency issue is mediated. The impact parameter of rebate factor in aggregator revenue in analyzed.

    关键字:Power grids;Economics;State of charge;Scheduling;Stakeholders;Real-time systems;Batteries

  • A two-stage electric vehicles scheduling strategy to address economic inconsistency issues of stakeholders

    Bing Han;Shaofeng Lu;Fei Xue;Lin Jiang;Huaiying Zhu;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Evaluation of safety indicators for truck platooning

    作者:Ellen van Nunen;Francesco Esposto;Arash Khabbaz Saberi;Jan-Pieter Paardekooper; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    This paper addresses safety indicators for truck platooning at short inter-vehicle distances (with a time gap of 0.5 s). The aim of a safety indicator is to determine the correct moment for initiating a Collision Avoidance brake action to prevent a collision with the preceding truck in threatening situations. Three safety indicators are selected for an evaluation: the intended acceleration of the preceding truck, which is shared via Vehicle-to-Vehicle (V2V) communication, the Brake Threat Number (BTN - based on simple vehicle models and an emergency brake assumption of the lead), and the Time-To-Collision (TTC - based on a constant velocity assumption). The latter two do not rely on V2V communication, but are obtained via on-board signals. Requirements for the amount of false negatives (missing a threatening situation) and the false positives (identifying a safe situation as threatening) are derived from a functional safety perspective. To find thresholds for the safety indicators that minimize the false negative rate, emergency brake tests are used. To evaluate the number of false positives, a set of data of two trucks driving in a platoon at 0.5 s at mixed-traffic highways in Belgium and the Netherlands, collected during 8 hours of automated driving in a platoon, is used. The results indicate that the communicated intended acceleration of the preceding truck might be able to distinguish safe and threatening situations in a vehicle platoon. Furthermore, for situations without V2V, both the BTN and the TTC are not capable to distinguish between threatening and safe situations. The amount of false positives found in the safe driving data-set does not fulfill the requirements derived from functional safety perspective.

    关键字:Safety;Collision avoidance;Brakes;Vehicles;Acceleration;Hardware;Roads

  • Evaluation of safety indicators for truck platooning

    Ellen van Nunen;Francesco Esposto;Arash Khabbaz Saberi;Jan-Pieter Paardekooper;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Evaluation of a semi-autonomous lane departure correction system using naturalistic driving data

    作者:Ding Zhao;Wenshuo Wang;David J. LeBlanc; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    Evaluating the effectiveness and benefits of driver assistance systems is essential for improving the system performance. In this paper, we propose an efficient evaluation method for a semi-autonomous lane departure correction system. To achieve this, we apply a bounded Gaussian mixture model to describe drivers' stochastic lane departure behavior learned from naturalistic driving data, which can regenerate departure behaviors to evaluate the lane departure correction system. In the stochastic lane departure model, we conduct a dimension reduction to reduce the computation cost. Finally, to show the advantages of our proposed evaluation approach, we compare steering systems with and without lane departure assistance based on the stochastic lane departure model. The simulation results show that the proposed method can effectively evaluate the lane departure correction system.

    关键字:Data models;Vehicles;Wheels;Gaussian mixture model;Roads

  • Evaluation of a semi-autonomous lane departure correction system using naturalistic driving data

    Ding Zhao;Wenshuo Wang;David J. LeBlanc;

    IEEE Intelligent Vehicles Symposium

    2017年

  • Evolutionary algorithm for positioning cameras networks mounted on UAV

    作者:David Strubel;Olivier Morel;Naufal M. Saad;David Fofi; 会议名称:IEEE Intelligent Vehicles Symposium 2017年

    This paper aims to optimize the coverage of a given area from a set of views to allow a complete mosaicing. Among the investigated methods to find the best camera positions, two of them are studied, namely the Particle Swarm Optimization (PSO) and the Genetic Algorithms (GA). After having performed experiments to compare the algorithms, the hybridization of GA and PSO is investigated. To validate the proposed method, it is simulated area of irregular shapes with the cameras mounted on a Unmanned Aerial Vehicles (UAVs). V-REP is used to simulate the UAVs in an indoor environment and satellite images are used for a large outdoor area. The simulation validates the efficiency of the proposed method to find the optimal position of cameras. Then by using the images acquired it is possible to monitor the area and to compute a full mosaic of it.

    关键字:Cameras;Genetic algorithms;Cost function;Biological cells;Shape;Robot sensing systems;Algorithm design and analysis

  • Evolutionary algorithm for positioning cameras networks mounted on UAV

    David Strubel;Olivier Morel;Naufal M. Saad;David Fofi;

    IEEE Intelligent Vehicles Symposium

    2017年

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(4)非以商业营利为目的承担正常教学任务的授权用户,为教学需要,可少量复制文献供教学人员使用,同时应指明作者姓名和文献名称。例如授权用户可少量复制作为教学参考资料的文献供从事课堂教授的教师使用,可少量文献复制到供本单位教学使用的内部网络中的安全计算机上,供选修特定课程的学生在该课程课堂教学期间和地点通过内部网络进行阅读。

3.侵权行为

除上述合理使用方式外,不得以任何方式对本平台全部或者部分文献进行复制、出版、发行、传播、转让、商业或其他开发,授权用户以下(但不仅限于以下)超出合理使用范围的行为,属侵犯知识产权行为,须严格禁止:

(1)把外文文献以公共方式提供给非授权用户使用;

(2)利用外文文献对非授权用户提供文献服务;

(3)利用外文文献进行商业服务或支持商业服务;

(4)利用外文文献内容汇编生成二次产品,提供公共或商业服务;

(5)将外文文献使用在任何其他网站或计算机联网环境中,包括但不仅限于在国际互联网和万维网站上。

4.侵权责任

授权用户应在法律规定的其超出合理使用范围内使用本平台提供的文献,若因授权用户的侵权使用行为被相关权利人主张权利而产生任何纠纷、仲裁、诉讼,或遭到国家相关部门处理,由授权用户承担全部法律责任并使本平台方免责,授权用户还应赔偿因上述原因给本平台方造成的全部直接和间接损失,包括但不限于律师费用、诉讼及仲裁费用、财务费用及差旅费等。一经发现用户的使用情况超出著作权法规定的合理使用范围,本平台有权立即封禁该用户的服务账户,并有权终止向用户提供本协议约定的服务而无须承担违约责任。

5.免责声明

(1)本平台仅为根据您的搜索指令提供搜索服务、数据链接服务和电子邮件原文传递服务,搜索结果和原文数据均来自第三方网站,六维联合不会对第三方网站内容作任何实质性的编辑、整理、修改;

(2)本平台对服务及服务所涉文献信息不作任何明示或暗示的保证,包括但不限于服务一定会满足用户的使用要求、服务的及时性、持续性、安全性、准确性以及服务所涉及文献的准确性、完整行、可靠性;

(3)本平台不保证所提供的服务和服务所涉文献信息来源客观准确,对于任何因信赖或使用获取的文献信息,而给用户造成任何直接或间接损失,本平台均不承担责任;本平台不因用户与任何第三方产生的争议或诉讼承担任何责任。

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6.其他规定

(1)本协议内容受中华人民共和国法律的约束。如果本协议中任何规定被裁定为无效或不可强制执行,该项规定应被撤销,而其余规定继续有效。

(2)本协议的最终解释权归六维联合。

(3)本协议条款可由六维联合随时全部或部分转让。未经六维联合事先明确书面同意,用户不得以任何方式转让本协议条款。

注意:六维联合未就您或其他人士的某项违约行为采取行动,并不表明六维联合就任何继后或类似的违约事件放弃采取行动的权利。


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