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PSI: A Pedestrian Behavior Dataset for Socially Intelligent Autonomous Car [article]

Tina Chen, Taotao Jing, Renran Tian, Yaobin Chen, Joshua Domeyer, Heishiro Toyoda, Rini Sherony, Zhengming Ding
2022 arXiv   pre-print
The first novel label is the dynamic intent changes for the pedestrians to cross in front of the ego-vehicle, achieved from 24 drivers with diverse backgrounds.  ...  This paper proposes and shares another benchmark dataset called the IUPUI-CSRC Pedestrian Situated Intent (PSI) data with two innovative labels besides comprehensive computer vision labels.  ...  The model consists of five modules from left to right: Context Feature Extraction, Visual Reasoning Estimation, Pedestrian Intent Estimation, Human Disagreement Estimation, and Pedestrian Trajectory Prediction  ... 
arXiv:2112.02604v2 fatcat:jckvyyh2yfdijomxckxyxjtiw4

A heuristic model for pedestrian intention estimation

Fanta Camara, Natasha Merat, Charles W. Fox
2019 2019 IEEE Intelligent Transportation Systems Conference (ITSC)  
intent on pedestrian-vehicle trajectory data for the first time.  ...  The method can predict pedestrian crossing intent, crossing or stopping, with 96% accuracy by the time the pedestrian reaches the curbside, on the standard Daimler pedestrian dataset.  ...  Results obtained from Model B for pedestrian crossing scenario (a) Interaction 3 (b) Estimated trajectories in meters (from 16 th frame) (c) Estimated trajectories in discrete cells (from 16 th frame)  ... 
doi:10.1109/itsc.2019.8917195 dblp:conf/itsc/CamaraMF19 fatcat:ziysrx5prrgtxgvdweo7eqka5y

Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM [article]

Zhe Huang, Aamir Hasan, Kazuki Shin, Ruohua Li, Katherine Driggs-Campbell
2020 arXiv   pre-print
Critical insights from human intention and behavioral patterns need to be integrated to effectively forecast long-term pedestrian behavior.  ...  Thus, we propose a framework incorporating a Mutable Intention Filter and a Warp LSTM (MIF-WLSTM) to simultaneously estimate human intention and perform trajectory prediction.  ...  Intention Filter. In practice, trajectory data is recorded once the pedestrian is detected.  ... 
arXiv:2007.00113v2 fatcat:cta6iavb4rhqbpisw66hquzeai

An Adaptive Motion Model for Person Tracking with Instantaneous Head-Pose Features

Rolf H. Baxter, Michael J. V. Leach, Sankha S. Mukherjee, Neil M. Robertson
2015 IEEE Signal Processing Letters  
We show that by using instantaneous 'intentional' priors our algorithm significantly outperforms a standard Kalman Filter on comprehensive test data.  ...  We apply this new method to pedestrian surveillance, using automatically-derived head pose estimates, although the theory is not limited to head-pose priors.  ...  Mean and variance were extracted for 37 pedestrians from the caviar dataset, 34 pedestrians from the PETS dataset, and 154 pedestrians from the Benfold dataset.  ... 
doi:10.1109/lsp.2014.2364458 fatcat:iw7jsgdb6jbmfknwqim2ins5cq

A Survey on Motion Prediction of Pedestrians and Vehicles for Autonomous Driving

Mahir Gulzar, Yar Muhammad, Naveed Muhammad
2021 IEEE Access  
These models extract contextual and situational information from the data and refine motion estimates accordingly.  ...  Learning-based approaches learn from data and statistics and recognize motion patterns from learned models.  ... 
doi:10.1109/access.2021.3118224 fatcat:igidt65lgjhjtnjjq2uvv32p24

Early warning of pedestrians and cyclists [article]

Joerg Christian Wolf
2021 arXiv   pre-print
We have identified that predicting the intention of a pedestrian reliably by position is a particularly hard challenge. This paper describes an early pedestrian warning demonstration system.  ...  State-of-the-art motor vehicles are able to break for pedestrians in an emergency. We investigate what it would take to issue an early warning to the driver so he/she has time to react.  ...  Example Data The following six figures illustrate pedestrian trajectories using data from the ADAS camera at different distances and vehicle speeds.  ... 
arXiv:2107.05186v1 fatcat:zjyncyna4bcb3eorwvzzsbkz3i

Probabilistic Prediction of Pedestrian Crossing Intention using Roadside LiDAR data

Junxuan Zhao, Yinfeng Li, Hao Xu, Hongchao Liu
2019 IEEE Access  
Pedestrians' crossing intention was predicted at a range of 0.5-3 s before actual crossings.  ...  INDEX TERMS Confidence level, Naïve Bayes, pedestrian crossing intention, roadside LiDAR. VOLUME 7, 2019 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  Pedestrian trajectories were the initial data for pedestrian crossing intention prediction.  ... 
doi:10.1109/access.2019.2927889 fatcat:4kzquuzxyfednnjehzwoofkc54

Online monitoring for safe pedestrian-vehicle interactions [article]

Peter Du, Zhe Huang, Tianqi Liu, Ke Xu, Qichao Gao, Hussein Sibai, Katherine Driggs-Campbell, Sayan Mitra
2020 arXiv   pre-print
We present a pedestrian intent estimation framework that can accurately predict future pedestrian trajectories given multiple possible goal locations.  ...  We investigate two key questions: How can we effectively integrate pedestrian intent estimation into our autonomous stack.  ...  Fig. 3 : 3 Detected pedestrian and position estimates. Fig. 4 : 4 Pedestrian intent estimation for scenario with three goals (red, yellow, green). Left: Predicted trajectory.  ... 
arXiv:1910.05599v2 fatcat:izgasmjekbd4lhcmtcfzr64dm4

Modified Driving Safety Field Based on Trajectory Prediction Model for Pedestrian–Vehicle Collision

Renfei Wu, Xunjia Zheng, Yongneng Xu, Wei Wu, Guopeng Li, Qing Xu, Zhuming Nie
2019 Sustainability  
A Dynamic Bayesian Network (DBN) model is employed for pedestrian intention inference, and a particle filtering model is conducted to simulate pedestrian motion.  ...  Driving data collection was conducted and pedestrian–vehicle scenarios were extracted. The effectiveness of the proposed model was evaluated by Monte Carlo simulations running 1000 times.  ...  Particle filtering was then used for predicting the future trajectories in following 3.0 s with the estimated crossing-intention of the pedestrian.  ... 
doi:10.3390/su11226254 fatcat:jsxnubq7zbbqtejn6buxwmy634

Roadside pedestrian motion prediction using Bayesian methods and particle filter

Qing Xu, Haoran Wu, Jianqiang Wang, Hui Xiong, Jinxin Liu, Keqiang Li
2021 IET Intelligent Transport Systems  
The results show that this method can give an accurate distribution of pedestrians' future trajectories.  ...  The proposed method predicts pedestrian motion based on the combination of pedestrian crossing behaviour and intention.  ...  If so, the speed estimation is doubted and the average speed is used for trajectory prediction.  ... 
doi:10.1049/itr2.12090 fatcat:ikfhrdnwrva4lgxnsoplwqpjfe

LOKI: Long Term and Key Intentions for Trajectory Prediction [article]

Harshayu Girase, Haiming Gang, Srikanth Malla, Jiachen Li, Akira Kanehara, Karttikeya Mangalam, Chiho Choi
2021 arXiv   pre-print
This is mainly because very few public datasets are available, and they only consider pedestrian-specific intents for a short temporal horizon from a restricted egocentric view.  ...  We show our method outperforms state-of-the-art trajectory prediction methods by upto 27% and also provide a baseline for frame-wise intention estimation.  ...  Acknowledgement We thank our Honda Research Institute USA colleagues -Behzad Dariush for his advice and support, Jiawei Huang for sensor calibration, and Huan Doung Nugen for data inspection and quality  ... 
arXiv:2108.08236v3 fatcat:syux3bd2zrdebozpeni6u7mtf4

A novel method of predictive collision risk area estimation for proactive pedestrian accident prevention system in urban surveillance infrastructure [article]

Byeongjoon Noh, Hwasoo Yeo
2021 arXiv   pre-print
The proposed system applied trajectories of vehicles and pedestrians from video footage after preprocessing, and then predicted their trajectories by using deep LSTM networks.  ...  In this study, we propose a predictive collision risk area estimation system at unsignalized crosswalks.  ...  The authors in [14] predicted pedestrians' red-light crossing intention in a crosswalk by using video data from real traffic scenes.  ... 
arXiv:2105.02572v1 fatcat:c2uzm4wmwrh3jmyxsi36tgwdhe

Prediction of Pedestrian Spatiotemporal Risk Levels for Intelligent Vehicles: A Data-driven Approach [article]

Zheyu Zhang, Boyang Wang, Chao Lu, Jinghang Li, Cheng Gong, Jianwei Gong
2021 arXiv   pre-print
Existing methods use either predefined collision-based models or human-labeling approaches to estimate the pedestrians' risks.  ...  This work tackles the listed problems by proposing a Pedestrian Risk Level Prediction system. The system consists of three modules. Firstly, vehicle-perspective pedestrian data are collected.  ...  Essentially, Module 2 builds on data to provide a long-term estimation of the interactive intention of both the ego vehicle and the pedestrian.  ... 
arXiv:2111.03822v1 fatcat:6mhqjnluknaqrc54nd6ldwcdri

Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence [article]

Maarten Bieshaar, Günther Reitberger, Stefan Zernetsch, Bernhard Sick, Erich Fuchs, Konrad Doll
2018 arXiv   pre-print
The intention detection consists of basic movement primitive prediction, e.g. standing, moving, turning, and a forecast of the future trajectory.  ...  Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an important role in future traffic.  ...  fuse intention estimates of other agents with own estimates.  ... 
arXiv:1809.03916v1 fatcat:gtc4v42cdbgyfjc4ddwgqriiie

Insights and Strategies for an Autonomous Vehicle with a Sensor Fusion Innovation: A Fictional Outlook

Farrukh Hafeez, Usman U. Sheikh, Nasir Khaldi, Hassan Z. Al Garni, Zeeshan A. Arfeen, Saifulnizam A.Khalid
2020 IEEE Access  
INDEX TERMS Advanced driver assistance system, deep learning, pedestrian intention prediction, sensor, sensor fusion.  ...  Along with sensor fusion, another area of prime importance that is necessary to be explored is the prediction of pedestrian intentions.  ...  Deep learning has been used and has obtained significant results in estimation of pedestrian intentions.  ... 
doi:10.1109/access.2020.3010940 fatcat:srcqf2a7gzfihilhvorrdgpvnq
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