2023 IEEE Intelligent Vehicles Symposium (IV)最新文献_第9页

AOP-Net: All-in-One Perception Network for LiDAR-based Joint 3D Object Detection and Panoptic Segmentation AOP-Net:基于激光雷达的联合三维目标检测和全视分割的一体化感知网络

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186743

Yixuan Xu, H. Fazlali, Y. Ren, Bingbing Liu

{"title":"AOP-Net: All-in-One Perception Network for LiDAR-based Joint 3D Object Detection and Panoptic Segmentation","authors":"Yixuan Xu, H. Fazlali, Y. Ren, Bingbing Liu","doi":"10.1109/IV55152.2023.10186743","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186743","url":null,"abstract":"LiDAR-based 3D object detection and panoptic segmentation are two crucial tasks in the perception systems of autonomous vehicles and robots. In this paper, we propose All-in-One Perception Network (AOP-Net), a LiDAR-based multitask framework that combines 3D object detection and panoptic segmentation. In this method, a dual-task 3D backbone is developed to extract both panoptic- and detection-level features from the input LiDAR point cloud. Also, a new 2D backbone that intertwines Multi-Layer Perceptron (MLP) and convolution layers is designed to further improve the detection task performance. Finally, a novel module is proposed to guide the detection head by recovering useful features discarded during down-sampling operations in the 3D backbone. This module leverages estimated instance segmentation masks to recover detailed information from each candidate object. The AOP-Net achieves state-of-the-art performance for published works on the nuScenes benchmark for both 3D object detection and panoptic segmentation tasks. Also, experiments show that our method easily adapts to and significantly improves the performance of any BEV-based 3D object detection method.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"102 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":"123528723","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

LiDAR-based Cooperative Relative Localization 基于激光雷达的协同相对定位

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186549

Jiqian Dong, Qi Chen, Deyuan Qu, Hongsheng Lu, Akila Ganlath, Qing Yang, Sikai Chen, S. Labi

{"title":"LiDAR-based Cooperative Relative Localization","authors":"Jiqian Dong, Qi Chen, Deyuan Qu, Hongsheng Lu, Akila Ganlath, Qing Yang, Sikai Chen, S. Labi","doi":"10.1109/IV55152.2023.10186549","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186549","url":null,"abstract":"Vehicular cooperative perception aims to provide connected and automated vehicles (CAVs) with a longer and wider sensing range, making perception less susceptible to occlusions. However, this prospect is dimmed by the imperfection of onboard localization sensors such as Global Navigation Satellite Systems (GNSS), which can cause errors in aligning over-the-air perception data (from a remote vehicle) with a Host vehicle’s (HV’s) local observation. To mitigate this challenge, we propose a novel LiDAR-based relative localization framework based on the iterative closest point (ICP) algorithm. The framework seeks to estimate the correct transformation matrix between a pair of CAVs’ coordinate systems, through exchanging and matching a limited yet carefully chosen set of point clouds and usage of a coarse 2D map. From the deployment perspective, this means our framework only consumes conservative bandwidth in data transmission and can run efficiently with limited resources. Extensive evaluations on both synthetic dataset (COMAP) and KITTI-360 show that our proposed framework achieves state-of-the-art (SOTA) performance in cooperative localization. Therefore, it can be integrated with any upper-stream data fusion algorithm and serves as a preprocessor for high-quality cooperative perception.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"8 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":"123882918","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

End-to-End Spatio-Temporal Attention-Based Lane-Change Intention Prediction from Multi-Perspective Cameras 基于多视角相机端到端时空注意力的变道意图预测

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186602

Zhouqiao Zhao, Zhensong Wei, Danyang Tian, B. Reimer, Pnina Gershon, Ehsan Moradi-Pari

{"title":"End-to-End Spatio-Temporal Attention-Based Lane-Change Intention Prediction from Multi-Perspective Cameras","authors":"Zhouqiao Zhao, Zhensong Wei, Danyang Tian, B. Reimer, Pnina Gershon, Ehsan Moradi-Pari","doi":"10.1109/IV55152.2023.10186602","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186602","url":null,"abstract":"Advanced Driver Assistance Systems (ADAS) with proactive alerts have been used to increase driving safety. Such systems’ performance greatly depends on how accurately and quickly the risky situations and maneuvers are detected. Existing ADAS provide warnings based on the vehicle’s operational status, detection of environments, and the drivers’ overt actions (e.g., using turn signals or steering wheels), which may not give drivers as much as optimal time to react. In this paper, we proposed a spatio-temporal attention-based neural network to predict drivers’ lane-change intention by fusing the videos from both in-cabin and forward perspectives. The Convolutional Neural Network (CNN)-Recursive Neural Network (RNN) network architecture was leveraged to extract both the spatial and temporal information. On top of this network backbone structure, the feature maps from different time steps and perspectives were fused using multi-head self-attention at each resolution of the CNN. The proposed model was trained and evaluated using a processed subset of the MIT Advanced Vehicle Technology (MIT-AVT) dataset which contains synchronized CAN data, 11058-second videos from 3 different views, 548 lane-change events, and 274 non-lane-change events performed by 83 drivers. The results demonstrate that the model achieves 87% F1-score within the 1-second validation window and 70% F1-score within the 5-second validation window with real-time performance.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"6 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":"124746273","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

Cooperative Reinforcement Learning-based Damping of Lane-Change-Induced Waves 基于协同强化学习的变道波阻尼研究

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186805

Kathy Jang, Y. Farid, K. Oguchi

引用次数: 0

Cooperative Merging Speed Planning: A Vehicle-Dynamics-Free Method 协同归并速度规划:一种无车辆动力学的方法

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186541

Zejiang Wang, Adian Cook, Y. Shao, Guanhao Xu, J. Chen

引用次数: 1

Investigating a Pressure Sensitive Surface Layer for Vehicle Localization 一种用于车辆定位的压力敏感面层研究

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186582

Simon Schäfer, Hendrik Steidl, S. Kowalewski, Bassam Alrifaee

引用次数: 0

Consensus-Based Fault-Tolerant Platooning for Connected and Autonomous Vehicles 基于共识的网联和自动驾驶车辆容错队列

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186667

Tzu-Yen Tseng, Ding Huang, Jia-You Lin, Po-Jui Chang, Chung-Wei Lin, Changliu Liu

引用次数: 0

Changing the Perspective: Assessing the Controllability of Minimal Risk Maneuvers for Surrounding Traffic Participants 改变视角:评估周边交通参与者最小风险机动的可控性

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186745

B. Karakaya, K. Bengler

引用次数: 0

Automatic Disengagement Scenario Reconstruction Based on Urban Test Drives of Automated Vehicles 基于自动驾驶汽车城市试驾的自动脱离场景重构

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186640

Zhijing Zhu, Robin Philipp, Yongqi Zhao, Constanze Hungar, Jürgen Pannek, Falk Howar

{"title":"Automatic Disengagement Scenario Reconstruction Based on Urban Test Drives of Automated Vehicles","authors":"Zhijing Zhu, Robin Philipp, Yongqi Zhao, Constanze Hungar, Jürgen Pannek, Falk Howar","doi":"10.1109/IV55152.2023.10186640","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186640","url":null,"abstract":"In recent years, scenario-based testing has gained increased attention as a potentially efficient strategy for validating the overall safety of automated vehicles. However, which scenarios are of interest for testing and how to systematically generate the test instances remain as unanswered questions. In this work, we interpret the importance of incorporating automated vehicle disengagement scenarios into scenario-based testing. Accordingly, we design and implement a fully automatic pipeline to reconstruct the essential and error-reduced disengagement scenarios based on imperfect perception measurement data from real test drives in an urban environment. Our concept is developed based on 137 disengagement data snippets and two additional datasets for handling false positives and false negatives in the original measurements. We use additional disengagement snippets for validating the performance of the pipeline. We exhibit representative reconstructed scenarios to show a successful restoration of the reality and quantitatively demonstrate the correct functioning of the methods in the pipeline regarding filtering irrelevant objects and handling the perception inaccuracies.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"59 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":"114478531","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

Human-Machine Interface Evaluation Using EEG in Driving Simulator 基于脑电的驾驶模拟器人机界面评价

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186567

Yuan-Cheng Liu, Nikol Figalová, M. Baumann, K. Bengler

引用次数: 0