2023 IEEE Intelligent Vehicles Symposium (IV)最新文献_第4页

Exploring Energy Impacts of Cyberattacks on Adaptive Cruise Control Vehicles 探索网络攻击对自适应巡航控制车辆的能源影响

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186730

Tianyi Li, Benjamin Rosenblad, Shian Wang, Mingfeng Shang, Raphael E. Stern

{"title":"Exploring Energy Impacts of Cyberattacks on Adaptive Cruise Control Vehicles","authors":"Tianyi Li, Benjamin Rosenblad, Shian Wang, Mingfeng Shang, Raphael E. Stern","doi":"10.1109/IV55152.2023.10186730","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186730","url":null,"abstract":"The emergence of automated vehicles (AVs) with driver-assist features, such as adaptive cruise control (ACC) and other automated driving capabilities, promises a bright future for transportation systems. However, these emerging features also introduce the possibility of cyberattacks. A select number of ACC vehicles could be compromised to drive abnormally, causing a network-wide impact on congestion and fuel consumption. In this study, we first introduce two types of candidate attacks on ACC vehicles: malicious attacks on vehicle control commands and false data injection attacks on sensor measurements. Then, we examine the energy impacts of these candidate attacks on distinct traffic conditions involving both free flow and congested regimes to get a sense of how sensitive the flow is to these candidate attacks. Specifically, the widely used VT-Micro model is adopted to quantify vehicle energy consumption. We find that the candidate attacks introduced to ACC or partially automated vehicles may only adversely impact the fuel consumption of the compromised vehicles and may not translate to significantly higher emissions across the fleet.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"76 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":"129129306","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

Data-driven Predictive Connected Cruise Control 数据驱动的预测连接巡航控制

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186677

Minghao Shen, G. Orosz

引用次数: 0

Design of an Experiment to Pinpoint Cognitive Failure Processes in the Interaction of Motorists and Vulnerable Road Users 一个实验的设计，以查明在驾驶者和弱势道路使用者的互动认知失败过程

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186550

Florian Denk, Felix Fröhling, Pascal Brunner, W. Huber, M. Margreiter, K. Bogenberger, R. Kates

{"title":"Design of an Experiment to Pinpoint Cognitive Failure Processes in the Interaction of Motorists and Vulnerable Road Users","authors":"Florian Denk, Felix Fröhling, Pascal Brunner, W. Huber, M. Margreiter, K. Bogenberger, R. Kates","doi":"10.1109/IV55152.2023.10186550","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186550","url":null,"abstract":"Background: Driving in urban traffic requires advanced cognitive skills: perceiving all relevant traffic participants, anticipating their likely trajectories, deciding which action to take, and controlling the vehicle. The underlying perceptual and cognitive processes are subject to occasional failures, which can depend in a complex way on learned heuristics and the cognitive load. Collisions between motor vehicles and vulnerable road users (VRU) in urban traffic remain frequent and have severe consequences. In this article, we study the behavior of drivers of motor vehicles turning right who are required to yield to cyclists riding straight through an intersection. A key potential error process is failure to perceive the cyclist.Methods: We conducted a trial with n = 35 subjects on our closed test track including observations of perceptual actions and gaze control, subject to variations in cognitive load and other factors. The artificial environment of a closed test track and the constraints due to ethical requirements pose challenges to the interpretation of any empirical trial. The current paper focuses on the trial design and on quantification of measurement validity.Results: Summary statistics involving trial features were assessed. Most participants reported that they performed the visual task of checking for cyclists in a manner similar to their behavior in real traffic (whether or not cyclist interactions were expected). The spatial distributions of driver glances to perceive cyclists were evaluated.Conclusion: The realism in this trial despite laboratory conditions may be attributable to ingrained skills and habits of participants. Laboratory trials can help to identify root causes of cognitive errors and ultimately guide efficient and effective deployment of bicycle safety countermeasures.","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":"125149980","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 Comprehensive Framework for Evaluating Vision-Based on-Board Rail Track Detection 基于视觉的车载轨道检测综合评估框架

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186659

Markus Ziegler, Vishal Mhasawade, Martin Köppel, Philipp Neumaier, Volker Eiselein

引用次数: 0

Refining Obstacle Perception Safety Zones via Maneuver-Based Decomposition 通过基于机动的分解改进障碍物感知安全区域

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186702

Sever Topan, Yuxiao Chen, E. Schmerling, Karen Leung, Jonas Nilsson, Michael Cox, M. Pavone

{"title":"Refining Obstacle Perception Safety Zones via Maneuver-Based Decomposition","authors":"Sever Topan, Yuxiao Chen, E. Schmerling, Karen Leung, Jonas Nilsson, Michael Cox, M. Pavone","doi":"10.1109/IV55152.2023.10186702","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186702","url":null,"abstract":"A critical task for developing safe autonomous driving stacks is to determine whether an obstacle is safety-critical, i.e., poses an imminent threat to the autonomous vehicle. Our previous work showed that Hamilton Jacobi reachability theory can be applied to compute interaction-dynamics-aware perception safety zones that better inform an ego vehicle’s perception module which obstacles are considered safety-critical. For completeness, these zones are typically larger than absolutely necessary, forcing the perception module to pay attention to a larger collection of objects for the sake of conservatism. As an improvement, we propose a maneuver-based decomposition of our safety zones that leverages information about the ego maneuver to reduce the zone volume. In particular, we propose a \"temporal convolution\" operation that produces safety zones for specific ego maneuvers, thus limiting the ego’s behavior to reduce the size of the safety zones. We show with numerical experiments that maneuver-based zones are significantly smaller (up to 76% size reduction) than the baseline while maintaining completeness.","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":"127562172","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

Effect of Music Intervention Strategies on Mitigating Drivers’ Negative Emotion in Post-congestion Driving 音乐干预策略对缓解拥堵后驾驶员负性情绪的影响

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186669

Delin Ouyang, Guofa Li, Qingkun Li, Xiaoxuan Sui, Xingda Qu, Gang Guo

{"title":"Effect of Music Intervention Strategies on Mitigating Drivers’ Negative Emotion in Post-congestion Driving","authors":"Delin Ouyang, Guofa Li, Qingkun Li, Xiaoxuan Sui, Xingda Qu, Gang Guo","doi":"10.1109/IV55152.2023.10186669","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186669","url":null,"abstract":"Traffic congestion is a common phenomenon in city traffic, which may cause drivers' negative emotion to degrade driving safety. It has been reported that music can regulate human emotion and the influence of negative emotion continuously challenges driving safety in post-congestion traffic. Therefore, this study aims to examine the effect of different music intervention strategies on mitigating drivers' negative emotion in post-congestion driving. Three experiments (i.e., driving with soft music, driving with disco jockey (DJ) music, and driving without music) are designed to collect drivers' driving performance measures, eye movement and electroencephalogram (EEG) responses in post-congestion driving. The results show that the music intervention strategies influence drivers' eye movement and EEG responses to varying degrees but do not have distinct effect in driving performance measures. These obtained results indicate that designing personalized music intervention strategies might help mitigate drivers' negative emotion to increase driving safety and comfort.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"366 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":"134263620","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

Experimental Validation of Intent Sharing in Cooperative Maneuvering 协同机动中意图共享的实验验证

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186821

Hong Wang, S. Avedisov, O. Altintas, G. Orosz

引用次数: 0

A Survey of Teleoperation: Driving Feedback 遥控操作:驱动反馈研究

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186553

Lin Zhao, M. Nybacka, M. Rothhämel

引用次数: 0

Decision-Making Strategy Using Multi-Agent Reinforcement Learning for Platoon Formation in Agreement-Seeking Cooperation 基于多智能体强化学习的寻约合作组队决策策略

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186813

Eunjeong Hyeon, D. Karbowski, A. Rousseau

引用次数: 0

Explainable Driver Activity Recognition Using Video Transformer in Highly Automated Vehicle 基于视频变压器的高度自动化车辆驾驶员行为识别

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186584

Akash Sonth, Abhijit Sarkar, Hirva Bhagat, Lynn Abbott

{"title":"Explainable Driver Activity Recognition Using Video Transformer in Highly Automated Vehicle","authors":"Akash Sonth, Abhijit Sarkar, Hirva Bhagat, Lynn Abbott","doi":"10.1109/IV55152.2023.10186584","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186584","url":null,"abstract":"Distracted driving is one of the leading causes of road accidents. With the recent introduction of advanced driver assistance systems and L2 vehicles, the role of driver attention has gained renewed interest. It is imperative for vehicle manufacturers to develop robust systems that can identify distractions and aid in preventing such accidents in highly automated vehicles. This paper mainly focuses on studying secondary behaviors, and their relative complexity to develop a guide for auto manufacturers. In recent years, a few driver secondary action datasets and deep learning algorithms have been created to address this problem. Despite their success in many domains, Convolutional Neural Network based deep learning methods struggle to fully consider the overall context of an image, and focus on specific image features. We present the use of Video Transformers on two challenging datasets, one of them being a grayscale low-quality dataset. We also demonstrate how the novel concept of a Visual Dictionary can be used to understand the structural components of any secondary behavior. Finally, we validate different components of the visual dictionary by studying the attention modules of the transformer-based model and incorporating explainability in the computer vision model. An activity is decomposed into multiple small actions and attributes and the corresponding attention patches are highlighted in the input frame. Our code is available at github.com/VTTI/driver-secondary-action-recognition","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"114 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":"123541395","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