2023 IEEE Intelligent Vehicles Symposium (IV)最新文献_第5页

Varying Road Surface Condition Estimation in Ego and Adjacent Lanes 自我及相邻车道变化路面状况估计

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186540

Hasith Karunasekera, A. Ekström, Amanda Siklund, Erik Hansson, Filip Anjou, Max Adolfsson, Vincent Carlson, J. Sjöberg

引用次数: 0

CoLD Fusion: A Real-time Capable Spline-based Fusion Algorithm for Collective Lane Detection 冷融合:一种实时的基于样条的融合算法用于集体车道检测

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186632

Jörg Gamerdinger, Sven Teufel, G. Volk, O. Bringmann

引用次数: 0

A Tightly-Coupled GNSS RTK/INS Positioning Algorithm Based on Adaptive Lag Smoother 基于自适应滞后平滑的GNSS RTK/INS紧耦合定位算法

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186552

Cheng Ye, Wei Li, Yu Hu

{"title":"A Tightly-Coupled GNSS RTK/INS Positioning Algorithm Based on Adaptive Lag Smoother","authors":"Cheng Ye, Wei Li, Yu Hu","doi":"10.1109/IV55152.2023.10186552","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186552","url":null,"abstract":"How to take into account both the calculation cost and positioning accuracy when driving over a long distance in the scene of changing satellite visibility, such as urban areas and mountain roads, is a research topic worth of attention for intelligent vehicles. In this paper, a tightly-coupled RTK/INS positioning algorithm base on adaptive lag smoother is proposed. By combining the uncertainty of the state to be estimated at the current time and the quantitative score of satellite visibility, the lag-length in smoother can be adjusted adaptively, so as to ensure positioning accuracy while reducing the calculation cost of marginal benefit consumption as much as possible. The proposed algorithm is demonstrated in both the simulator and real-world urban roads. From the experimental results, it is found that the estimation accuracy achieved by the adaptive lag smoother is similar to that of smoothers with long lag length, but the time consumption is reduced by about 30%. Under the same condition that the positioning can be completed in real time, the accuracy of the algorithm in this paper is 27% higher than that of the tightly-coupled RTK/INS system based on extended Kalman filter.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129884032","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}

引用次数: 0

How Fast is My Software? Latency Evaluation for a ROS 2 Autonomous Driving Software 我的软件有多快?ROS 2自动驾驶软件的延迟评估

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186585

Tobias Betz, Maximilian Schmeller, Harun Teper, Johannes Betz

引用次数: 2

Holistic Driving Scenario Concept for Urban Traffic 城市交通整体驾驶场景概念

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186385

Hendrik Weber, C. Glasmacher, Michael Schuldes, Nicolas Wagener, L. Eckstein

引用次数: 0

Evidential deep learning-based multi-modal environment perception for intelligent vehicles 基于证据深度学习的智能车辆多模态环境感知

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186581

Mihreteab Negash Geletu, Danut-Vasile Giurgi, Thomas Josso-Laurain, M. Devanne, Mengesha Mamo Wogari, Jean-Philippe Lauffenburger

{"title":"Evidential deep learning-based multi-modal environment perception for intelligent vehicles","authors":"Mihreteab Negash Geletu, Danut-Vasile Giurgi, Thomas Josso-Laurain, M. Devanne, Mengesha Mamo Wogari, Jean-Philippe Lauffenburger","doi":"10.1109/IV55152.2023.10186581","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186581","url":null,"abstract":"Intelligent vehicles (IVs) are pursued in both research laboratories and industries to revolutionize transportation systems. Since the driving surroundings can be cluttered and the weather conditions may vary, environment perception in IVs represents a challenging task. Therefore, multi-modal sensors are engaged. In perception, outstanding performance is obtained by employing deep learning algorithms. However, deep learning often relies on probabilities while there is a better formalism to handle prediction uncertainty. To circumvent this, in this work, evidence theory is combined with a camera-lidar-based deep learning fusion architecture. The coupling is based on generating basic belief functions using distance to prototypes. It also uses a distance-based decision rule. Because IVs have constrained computational power, a reduced deep-learning architecture is leveraged in this formulation. In the task of road detection, the evidential approach outperforms the probabilistic one. Besides, ambiguous features can be prudently settled as ignorance rather than making a possibly wrong decision using probability. The coupling is also extended to the task of semantic segmentation. This shows how evidential formulation can be easily adapted to the multi-class case. Therefore, the evidential formulation is generic and produces a more accurate and versatile prediction while maintaining the trade-off between performances and computational costs in IVs. This work uses the KITTI dataset.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128510556","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}

引用次数: 0

LIV-DeepSORT: Optimized DeepSORT for Multiple Object Tracking in Autonomous Vehicles Using Camera and LiDAR Data Fusion live -DeepSORT:利用摄像头和激光雷达数据融合优化自动驾驶汽车多目标跟踪的DeepSORT

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186759

Z. Rakotoniaina, N. E. Chelbi, D. Gingras, Frédéric Faulconnier

{"title":"LIV-DeepSORT: Optimized DeepSORT for Multiple Object Tracking in Autonomous Vehicles Using Camera and LiDAR Data Fusion","authors":"Z. Rakotoniaina, N. E. Chelbi, D. Gingras, Frédéric Faulconnier","doi":"10.1109/IV55152.2023.10186759","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186759","url":null,"abstract":"Object detection and tracking play a crucial role in the perception systems of autonomous vehicles. Simple Online Real-Time (SORT) techniques, such as DeepSORT, have proven to be among the most effective methods for multiple object tracking (MOT) in computer vision due to their ability to balance high performance with robustness in challenging scenarios. This article presents a method for adapting and optimizing the DeepSORT tracking algorithm to meet the demands of autonomous driving applications. Our approach leverages the Mask-Mean algorithm [2] to fuse data from cameras and LiDARs, as well as to detect, segment, and extract the 3D positions of objects in real-world space. In objects tracking, we take into account the ego-vehicle’s motion to estimate each object’s state, and the Unscented Kalman Filter (UKF) is utilized to handle the nonlinearity of each object’s motion state in real-world space. Our optimized version of DeepSORT, known as LIV-DeepSORT, demonstrates its ability to track multiple objects with high levels of robustness and accuracy, even in dynamic environments, making it suitable for the perception systems of autonomous vehicles.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121033227","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}

引用次数: 0

Automatic Extrinsic Calibration of Thermal Camera and LiDAR for Vehicle Sensor Setups 车载传感器热像仪和激光雷达的自动外部定标

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186694

Farhad Dalirani, Farzan Heidari, Taufiq Rahman, Daniel Singh Cheema, Michael A. Bauer

{"title":"Automatic Extrinsic Calibration of Thermal Camera and LiDAR for Vehicle Sensor Setups","authors":"Farhad Dalirani, Farzan Heidari, Taufiq Rahman, Daniel Singh Cheema, Michael A. Bauer","doi":"10.1109/IV55152.2023.10186694","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186694","url":null,"abstract":"LiDAR is one of the most used sensors in many areas like robotics, self-driving cars, and advanced driving assistance systems due to providing an accurate point cloud of the surroundings. However, to cope with challenges in perceiving the environment around a vehicle, LiDAR data is often combined with data from other sensors. Thermal cameras can provide complementary information that can be beneficial, especially for detecting pedestrians and seeing at nighttime and in fog, dust, etc. In this paper, we propose an algorithm for the extrinsic calibration of a thermal camera and a LiDAR sensor in a vehicle. First, one or more thermal image-point cloud pairs of our designed calibration target are collected. Then line and plane equations of the target’s edges and plane in both data modalities are found. Finally, the algorithm uses lines and plane correspondences to cross-calibrate the sensors. The proposed method obtains good results with one or more poses. We also show that it works well with sparse LiDAR data. Several experiments are presented to illustrate the effectiveness of the method.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121281505","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}

引用次数: 0

A Novel Framework for Modeling and Synthesizing Stealthy Cyberattacks on Driver-Assist Enabled Vehicles 驾驶辅助车辆隐身网络攻击建模与综合的新框架

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186690

Shian Wang

{"title":"A Novel Framework for Modeling and Synthesizing Stealthy Cyberattacks on Driver-Assist Enabled Vehicles","authors":"Shian Wang","doi":"10.1109/IV55152.2023.10186690","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186690","url":null,"abstract":"While the first generation of driver-assist enabled vehicles, i.e., adaptive cruise control (ACC) vehicles, are becoming increasingly available, the emerging ACC technologies open a door for malicious cyberattacks, where a select number of ACC vehicles are compromised to drive in an adversarial fashion, degrading the performance of transportation systems. Many prior studies have assumed constant or stochastic attacks without much consideration of their malicious and stealthy nature. Consequently, some attacks may even act favorably to the compromised vehicles, appearing to be an unreasonable practice. To this end, we develop a novel framework for modeling and synthesizing a broad class of potential attacks with practical interpretation considering the attacker perspective. Being able to model and characterize malicious attacks on ACC vehicles is the first step towards developing effective detection and mitigation strategies as ACC vehicles continue to increase in the market. In this study, we first present a general framework describing mixed traffic involving ACC and human-driven vehicles (HDVs) based on car-following dynamics. Under this framework, a class of potential false data injection attacks on ACC sensor measurements are mathematically modeled and incorporated into traffic flow dynamics. Further, we analytically characterize their malicious and stealthy nature, resulting in a class, i.e., ${mathcal{C}}$, of physically interpretable attacks. To illustrate the modeling mechanism, we conduct numerical experiments to study how attacks drawn from the set ${mathcal{C}}$ and its complement impact car-following dynamics. In addition, the energy impact of attacks from ${mathcal{C}}$ on traffic flow is also examined considering different levels of attack severity.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125486074","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}

引用次数: 1

Detecting Data Spoofing in Connected Vehicle based Intelligent Traffic Signal Control using Infrastructure-Side Sensors and Traffic Invariants 基于基础设施侧传感器和交通不变量的网联车辆智能交通信号控制数据欺骗检测

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186689

Junjie Shen, Ziwen Wan, Y. Luo, Yiheng Feng, Z. Morley Mao, Qi Alfred Chen

引用次数: 1