2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)最新文献_第5页

Research on optimization and evaluation method of the car following model based on SUMO application test scenario 基于SUMO应用测试场景的汽车跟随模型优化与评价方法研究

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669238

Qianjing Sun, Yong Wang, Lingqiu Zeng, Qingwen Han, Qinglong Xie, L. Ye, Fukun Xie

引用次数: 3

Driving Behavior Aware Caption Generation for Egocentric Driving Videos Using In-Vehicle Sensors* 基于车载传感器的自我中心驾驶视频的驾驶行为感知字幕生成*

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669259

Hongkuan Zhang, Koichi Takeda, Ryohei Sasano, Yusuke Adachi, Kento Ohtani

{"title":"Driving Behavior Aware Caption Generation for Egocentric Driving Videos Using In-Vehicle Sensors*","authors":"Hongkuan Zhang, Koichi Takeda, Ryohei Sasano, Yusuke Adachi, Kento Ohtani","doi":"10.1109/ivworkshops54471.2021.9669259","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669259","url":null,"abstract":"Video captioning aims to generate textual descriptions according to the video contents. The risk assessment of autonomous driving vehicles has become essential for an insurance company for providing adequate insurance coverage, in particular, for emerging MaaS business. The insurers need to assess the risk of autonomous driving business plans with a fixed route by analyzing a large number of driving data, including videos recorded by dash cameras and sensor signals. To make the process more efficient, generating captions for driving videos can provide insurers concise information to understand the video contents quickly. A natural problem with driving video captioning is, since the absence of egovehicles in these egocentric videos, descriptions of latent driving behaviors are difficult to be grounded in specific visual cues. To address this issue, we focus on generating driving video captions with accurate behavior descriptions, and propose to incorporate in-vehicle sensors which encapsulate the driving behavior information to assist the caption generation. We evaluate our method on the Japanese driving video captioning dataset called City Traffic, where the results demonstrate the effectiveness of in-vehicle sensors on improving the overall performance of generated captions, especially on generating more accurate descriptions for the driving behaviors.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130792083","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

EEG-based System Using Deep Learning and Attention Mechanism for Driver Drowsiness Detection 基于脑电图的深度学习和注意机制驾驶员困倦检测系统

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669234

Miankuan Zhu, Haobo Li, Jiangfan Chen, Mitsuhiro Kamezaki, Zutao Zhang, Ze-xi Hua, S. Sugano

{"title":"EEG-based System Using Deep Learning and Attention Mechanism for Driver Drowsiness Detection","authors":"Miankuan Zhu, Haobo Li, Jiangfan Chen, Mitsuhiro Kamezaki, Zutao Zhang, Ze-xi Hua, S. Sugano","doi":"10.1109/ivworkshops54471.2021.9669234","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669234","url":null,"abstract":"The lack of sleep (typically <6 hours a night) or driving for a long time are the reasons of drowsiness driving and caused serious traffic accidents. With pandemic of the COVID-19, drivers are wearing masks to prevent infection from it, which makes visual-based drowsiness detection methods difficult. This paper presents an EEG-based driver drowsiness estimation method using deep learning and attention mechanism. First of all, an 8-channels EEG collection hat is used to acquire the EEG signals in the simulation scenario of drowsiness driving and normal driving. Then the EEG signals are pre-processed by using the linear filter and wavelet threshold denoising. Secondly, the neural network based on attention mechanism and deep residual network (ResNet) is trained to classify the EEG signals. Finally, an early warning module is designed to sound an alarm if the driver is judged as drowsy. The system was tested under simulated driving environment and the drowsiness detection accuracy of the test set was 93.35%. Drowsiness warning simulation also verified the effectiveness of proposed early warning module.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126387567","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: 6

Active Safety System for Semi-Autonomous Teleoperated Vehicles 半自动遥控车辆主动安全系统

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-06-28 DOI: 10.1109/ivworkshops54471.2021.9669239

Smit Saparia, Andreas Schimpe, L. Ferranti

{"title":"Active Safety System for Semi-Autonomous Teleoperated Vehicles","authors":"Smit Saparia, Andreas Schimpe, L. Ferranti","doi":"10.1109/ivworkshops54471.2021.9669239","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669239","url":null,"abstract":"Autonomous cars can reduce road traffic accidents and provide a safer mode of transport. However, key technical challenges, such as safe navigation in complex urban environments, need to be addressed before deploying these vehicles on the market. Teleoperation can help smooth the transition from human operated to fully autonomous vehicles since it still has human in the loop providing the scope of fallback on driver. This paper presents an Active Safety System (ASS) approach for teleoperated driving. The proposed approach helps the operator ensure the safety of the vehicle in complex environments, that is, avoid collisions with static or dynamic obstacles. Our ASS relies on a model predictive control (MPC) formulation to control both the lateral and longitudinal dynamics of the vehicle. By exploiting the ability of the MPC framework to deal with constraints, our ASS restricts the controller’s authority to intervene for lateral correction of the human operator’s commands, avoiding counter-intuitive driving experience for the human operator. Further, we design a visual feedback to enhance the operator’s trust over the ASS. In addition, we propose an MPC’s prediction horizon data based novel predictive display to mitigate the effects of large latency in the teleoperation system. We tested the performance of the proposed approach on a high-fidelity vehicle simulator in the presence of dynamic obstacles and latency.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115077067","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: 8

Influences on Drivers’ Understandings of Systems by Presenting Image Recognition Results 呈现图像识别结果对驾驶员对系统理解的影响

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-06-25 DOI: 10.1109/ivworkshops54471.2021.9669225

Bo Yang, K. Inoue, S. Kitazaki, Kimihiko Nakano

引用次数: 1

The Oxford Road Boundaries Dataset 牛津路边界数据集

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-06-16 DOI: 10.1109/ivworkshops54471.2021.9669250

Tarlan Suleymanov, Matthew Gadd, D. Martini, P. Newman

引用次数: 0

Real-World Evaluation of the Impact of Automated Driving System Technology on Driver Gaze Behavior, Reaction Time and Trust 自动驾驶系统技术对驾驶员注视行为、反应时间和信任影响的真实世界评估

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-06-14 DOI: 10.1109/ivworkshops54471.2021.9669230

Walter Morales-Alvarez, M. Marouf, H. Tadjine, C. Olaverri-Monreal

引用次数: 3

Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis 基于仿真的测试验证:用标签到图像合成绕过域移位

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-06-10 DOI: 10.1109/ivworkshops54471.2021.9669248

Julia Rosenzweig, E. Brito, H. Kobialka, M. Akila, Nico M. Schmidt, Peter Schlicht, Jan David Schneider, Fabian Hüger, M. Rottmann, Sebastian Houben, Tim Wirtz

{"title":"Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis","authors":"Julia Rosenzweig, E. Brito, H. Kobialka, M. Akila, Nico M. Schmidt, Peter Schlicht, Jan David Schneider, Fabian Hüger, M. Rottmann, Sebastian Houben, Tim Wirtz","doi":"10.1109/ivworkshops54471.2021.9669248","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669248","url":null,"abstract":"Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since simulations are prone to domain shift w.r.t. real-life data, it is crucial to verify the transferability of the obtained results.We propose a novel framework consisting of a generative label-to-image synthesis model together with different transferability measures to inspect to what extent we can transfer testing results of semantic segmentation models from synthetic data to equivalent real-life data. With slight modifications, our approach is extendable to, e.g., general multi-class classification tasks. Grounded on the transferability analysis, our approach additionally allows for extensive testing by incorporating controlled simulations. We validate our approach empirically on a semantic segmentation task on driving scenes. Transferability is tested using correlation analysis of IoU and a learned discriminator. Although the latter can distinguish between real-life and synthetic tests, in the former we observe surprisingly strong correlations of 0.7 for both cars and pedestrians.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131530960","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: 2

A Survey on Deep Domain Adaptation for LiDAR Perception 激光雷达感知深度域自适应研究进展

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-06-04 DOI: 10.1109/IVWorkshops54471.2021.9669228

Larissa T. Triess, M. Dreissig, Christoph B. Rist, J. M. Zöllner

{"title":"A Survey on Deep Domain Adaptation for LiDAR Perception","authors":"Larissa T. Triess, M. Dreissig, Christoph B. Rist, J. M. Zöllner","doi":"10.1109/IVWorkshops54471.2021.9669228","DOIUrl":"https://doi.org/10.1109/IVWorkshops54471.2021.9669228","url":null,"abstract":"Scalable systems for automated driving have to reliably cope with an open-world setting. This means, the perception systems are exposed to drastic domain shifts, like changes in weather conditions, time-dependent aspects, or geographic regions. Covering all domains with annotated data is impossible because of the endless variations of domains and the time-consuming and expensive annotation process. Furthermore, fast development cycles of the system additionally introduce hardware changes, such as sensor types and vehicle setups, and the required knowledge transfer from simulation.To enable scalable automated driving, it is therefore crucial to address these domain shifts in a robust and efficient manner. Over the last years, a vast amount of different domain adaptation techniques evolved. There already exists a number of survey papers for domain adaptation on camera images, however, a survey for LiDAR perception is absent. Nevertheless, LiDAR is a vital sensor for automated driving that provides detailed 3D scans of the vehicle’s surroundings. To stimulate future research, this paper presents a comprehensive review of recent progress in domain adaptation methods and formulates interesting research questions specifically targeted towards LiDAR perception.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134050618","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: 43

Online and Adaptive Parking Availability Mapping: An Uncertainty-Aware Active Sensing Approach for Connected Vehicles 在线自适应车位可用性映射:一种面向网联车辆的不确定性感知主动感知方法

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-05-01 DOI: 10.1109/ivworkshops54471.2021.9669241

Luca Varotto, A. Cenedese

引用次数: 0