2023 IEEE Intelligent Vehicles Symposium (IV)最新文献_第2页

Interaction-aware Maneuver Prediction for Autonomous Vehicles using Interaction Graphs 基于交互图的自动驾驶车辆交互感知机动预测

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186811

I. P. Gomes, C. Premebida, D. Wolf

引用次数: 0

Auto-tuning extended Kalman filters to improve state estimation 自调谐扩展卡尔曼滤波器，以改善状态估计

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186760

B. Boulkroune, K. Geebelen, Jia Wan, E. V. Nunen

引用次数: 0

Operational Design Domain for Automated Driving Systems: Taxonomy Definition and Application 自动驾驶系统的操作设计领域:分类、定义和应用

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186765

Léo Mendiboure, M. Benzagouta, D. Gruyer, Tidiane Sylla, Morayo Adedjouma, Abdelmename Hedhli

引用次数: 6

Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour 基于仿真的真实驾驶员行为反事实因果发现

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186705

Rhys Howard, L. Kunze

{"title":"Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour","authors":"Rhys Howard, L. Kunze","doi":"10.1109/IV55152.2023.10186705","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186705","url":null,"abstract":"Being able to reason about how one’s behaviour can affect the behaviour of others is a core skill required of intelligent driving agents. Despite this, the state of the art struggles to meet the need of agents to discover causal links between themselves and others. Observational approaches struggle because of the non-stationarity of causal links in dynamic environments, and the sparsity of causal interactions while requiring the approaches to work in an online fashion. Meanwhile interventional approaches are impractical as a vehicle cannot experiment with its actions on a public road. To counter the issue of non-stationarity we reformulate the problem in terms of extracted events, while the previously mentioned restriction upon interventions can be overcome with the use of counterfactual simulation. We present three variants of the proposed counterfactual causal discovery method and evaluate these against state of the art observational temporal causal discovery methods across 3396 causal scenes extracted from a real world driving dataset. We find that the proposed method significantly outperforms the state of the art on the proposed task quantitatively and can offer additional insights by comparing the outcome of an alternate series of decisions in a way that observational and interventional approaches cannot.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"46 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":"132412362","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

Latency Measurement for Autonomous Driving Software Using Data Flow Extraction 基于数据流提取的自动驾驶软件延迟测量

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186686

Tobias Betz, Maximilian Schmeller, Andreas Korb, Johannes Betz

引用次数: 2

Intention-Aware Lane Keeping Assist Using Driver Gaze Information 使用驾驶员注视信息的意图感知车道保持辅助

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186601

John Dahl, G. R. Campos, J. Fredriksson

{"title":"Intention-Aware Lane Keeping Assist Using Driver Gaze Information","authors":"John Dahl, G. R. Campos, J. Fredriksson","doi":"10.1109/IV55152.2023.10186601","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186601","url":null,"abstract":"A lane keeping assist system uses sensor and environmental information to automatically steer the vehicle, whenever necessary, to keep it within the lanes. As the system overrides the driver, it is important that automatic interventions are only used when the driver is unaware of the traffic situation, i.e., in cases of unintentional lane departures. Hence, one of the major challenges for such systems is to distinguish between intentional and unintentional driving behaviors. In this work, we implement an intention-aware lane keeping assist system based on machine learning, where the goal is to activate interventions only when the lane departure is unintentional. The system performance is evaluated using a real-world data set, partly consisting of unintentional lane departure events, normal driving, and intentional lane departure events. The results show that driver state information, obtained from a camera-based gaze-tracking system, improves the lane keeping assist system’s performance, especially for intentional lane departure events. They also show that it is hard to predict the driver intention for prediction horizons longer than 1.5 s.","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":"133989235","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

CommonRoad-CriMe: A Toolbox for Criticality Measures of Autonomous Vehicles 共同道路犯罪:自动驾驶汽车临界测量工具箱

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186673

Yuanfei Lin, M. Althoff

引用次数: 4

A review of vision-based road detection technology for unmanned vehicles 基于视觉的无人驾驶车辆道路检测技术综述

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186761

Chaoyang Liu, Xueyuan Li, Qi Liu, Fan Yang, Zirui Li, Mengkai Li

引用次数: 0

Interaction-aware Predictive Collision Detector for Human-aware Collision Avoidance 面向人感知避碰的交互感知预测碰撞检测器

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186778

Thomas Genevois, A. Spalanzani, C. Laugier

{"title":"Interaction-aware Predictive Collision Detector for Human-aware Collision Avoidance","authors":"Thomas Genevois, A. Spalanzani, C. Laugier","doi":"10.1109/IV55152.2023.10186778","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186778","url":null,"abstract":"With their progressive deployment in increasingly complex environments, autonomous vehicles will more often interact with humans in shared spaces. However proactive planners, the most effective for human-aware navigation, are rarely applicable with real-world constraints because of their inherent complexity. Meanwhile classical approaches fail to navigate in cooperation with humans in complex or crowded scenarios. Therefore we propose to extend a global kinodynamic predictive collision avoidance approach with an interaction-aware behavioral prediction model for human-vehicle interactions. Thanks to a grid based Bayesian perception, our approach is versatile in modeling uncertainty and complex scenes. We deploy this solution on a robotic car and show that it can be used in real-world applications. With a qualitative and quantitative validation, we show that this interaction-aware collision avoidance solution is safe and performs well in crowded scenarios. Less computationally demanding and more versatile than proactive planners but still able to benefit from cooperation with humans, this interaction-aware approach offers a compromise between predictive and proactive planners.","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":"128923067","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

Towards Active Motion Planning in Interactive Driving Scenarios: A Generic Utility Term of Interaction Activeness 走向交互式驾驶场景中的主动运动规划:一个交互式主动效用术语

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186564

X. Zhao, Meng Wang, Shiyu Fang, Jian Sun

引用次数: 0