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

Mahir Gulzar, Yar Muhammad, Naveed Muhammad
2021 IEEE Access  
road users, pedestrians being one of them which belong to the most vulnerable road user class [3] .  ...  Physics-based models are simpler to implement and have been in use for a long time but they usually lack in terms of enriching the model with contextual information, thus most of these models predict motion  ... 
doi:10.1109/access.2021.3118224 fatcat:igidt65lgjhjtnjjq2uvv32p24

Multi-task Deep Learning for Pedestrian Detection, Action Recognition and Time to Cross Prediction

Danut Ovidiu Pop, Alexandrina Rogozan, Clement Chatelain, Fawzi Nashashibi, Abdelaziz Bensrhair
2019 IEEE Access  
For each pedestrian, the recurrent network estimates the pedestrian's action intention in order to predict the time to cross the street.  ...  A pedestrian detection system is a crucial component of advanced driver assistance systems since it contributes to road flow safety.  ...  road users.  ... 
doi:10.1109/access.2019.2944792 fatcat:rujdpxk72fhznk3vhkik5rdrsy

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion [article]

Dongxu Guo, Taylor Mordan, Alexandre Alahi
2022 arXiv   pre-print
Considering the lack of suitable existing datasets for it, we release TRANS, a benchmark for explicitly studying the stop and go behaviors of pedestrians in urban traffic.  ...  We suggest that predicting these highly non-linear transitions should form a core component to improve the robustness of motion prediction algorithms.  ...  The contextual and environmental cues, which can provide crucial information for long-term prediction, are largely ignored.  ... 
arXiv:2203.02489v1 fatcat:auat4itsmfg47oqg6lvdfeu27y

Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs [article]

Amir Rasouli, Iuliia Kotseruba, John K. Tsotsos
2020 arXiv   pre-print
One of the major challenges for autonomous vehicles in urban environments is to understand and predict other road users' actions, in particular, pedestrians at the point of crossing.  ...  To this end, we propose a solution for the problem of pedestrian action anticipation at the point of crossing.  ...  In this context, pedestrians are of particular importance being the most vulnerable road users, especially when crossing the road.  ... 
arXiv:2005.06582v1 fatcat:g346gok6nrcbdfekwzy4yvq3dm

Front Matter: Volume 9474

2015 Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV  
Publication of record for individual papers is online in the SPIE Digital Library.  ...  SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model, with papers published first online and then in print.  ...  Cognitive models Predictive models Objectives of models [ Example: Cognitive Models of Intent Minimize uncertainty and maximize the value of deduced information to detect/identify potential intent  ... 
doi:10.1117/12.2202240 fatcat:h6ezdn7gkbgyxi732iaig3dfxa

Context-based cyclist path prediction using Recurrent Neural Networks

Ewoud A. I. Pool, Julian F. P. Kooij, Dariu M. Gavrila
2019 2019 IEEE Intelligent Vehicles Symposium (IV)  
This paper proposes a Recurrent Neural Network (RNN) for cyclist path prediction to learn the effect of contextual cues on the behavior directly in an end-to-end approach, removing the need for any annotations  ...  mode annotations, where our model attains an average prediction error of 33 cm one second into the future.  ...  Vulnerable Road Users (VRUs) (i.e. pedestrians and cyclists) are particularly challenging road users to deal with. They can rapidly switch between various motion modes, such as walking or standing.  ... 
doi:10.1109/ivs.2019.8813889 dblp:conf/ivs/PoolKG19 fatcat:ciuj7iwk7jdg5puqa6dmscbcce

Scanning the Issue

Azim Eskandarian
2020 IEEE transactions on intelligent transportation systems (Print)  
For further investigation in congested traffic stream, they simulated a group of vehicles by adding an error term to represent the driver's unexpected behavior.  ...  To evaluate the ACF model, they performed the simulation for car-following pairs and conducted a comparison analysis with the existing models: Newell, Gipps, GM, and IDM.  ...  Contextual Recurrent Predictive Model for Long-Term Intent Prediction of Vulnerable Road Users K. Saleh, M. Hossny, and S.  ... 
doi:10.1109/tits.2020.3008809 fatcat:etol5qoilvdnbj6gtjxk3gheaa

Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review [article]

Sajjad Mozaffari, Omar Y. Al-Jarrah, Mehrdad Dianati, Paul Jennings, Alexandros Mouzakitis
2020 arXiv   pre-print
Motivated by this increased popularity, we provide a comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behaviour prediction in this paper.  ...  Behaviour prediction function of an autonomous vehicle predicts the future states of the nearby vehicles based on the current and past observations of the surrounding environment.  ...  One part of this general problem is to predict the behaviour of pedestrians (or generally speaking, the vulnerable road-users), which is well-studied in computer vision literature [2] , [3] , [4] ,  ... 
arXiv:1912.11676v2 fatcat:7eiwrdorynfuvnxougquivegri

Emotion-awareness for intelligent vehicle assistants

Hans-Jörg Vögel, Raphaël Troncy, Benoit Huet, Melek Önen, Adlen Ksentini, Jörg Conradt, Asaf Adi, Alexander Zadorojniy, Jacques Terken, Jonas Beskow, Ann Morrison, Christian Süß (+11 others)
2018 Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems - SEFAIS '18  
design, cognitive modelling, decision making and recommender systems, emotion sensing as feedback for learning, and distributed (edge) computing delivering cognitive services.  ...  EVA1 is describing a new class of emotion-aware autonomous systems delivering intelligent personal assistant functionalities.  ...  Also depicted in Figure 1 is the need for these autonomous vehicles to communicate to and interact with vulnerable road users outside the vehicle.  ... 
doi:10.1145/3194085.3194094 fatcat:stdxabm2jjevbijg4vboixszze

Human Motion Trajectory Prediction: A Survey [article]

Andrey Rudenko, Luigi Palmieri, Michael Herman, Kris M. Kitani, Dariu M. Gavrila, Kai O. Arras
2019 arXiv   pre-print
We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual  ...  Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems.  ...  Lilienthal for valuable feedback and suggestions.  ... 
arXiv:1905.06113v3 fatcat:cnomix2fs5gqvb6ormldgti2bm

Detecting Human Driver Inattentive and Aggressive Driving Behavior using Deep Learning: Recent Advances, Requirements and Open Challenges

Monagi H. Alkinani, Wazir Zada Khan, Quratulain Arshad
2020 IEEE Access  
INDEX TERMS Deep learning, human inattentive driving behavior, connected vehicles, road accident avoidance, abnormal behavior detection, distraction or aggressiveness detection, fatigue or drowsiness detection  ...  However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network  ...  ACKNOWLEDGMENT The authors acknowledge the technical and financial support of University of Jeddah.  ... 
doi:10.1109/access.2020.2999829 fatcat:5nxtzm6yfbe4jf6nqgreqw45r4

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Bogdan Ilie Sighencea, Rareș Ion Stanciu, Cătălin Daniel Căleanu
2021 Sensors  
The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs  ...  Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21227543 pmid:34833619 pmcid:PMC8619260 fatcat:u7wapci74jdlpcbpn2uu5ljomy

Behavior Prediction of Traffic Actors for Intelligent Vehicle using Artificial Intelligence Techniques: A Review

Suresh Kolekar, Shilpa Gite, Biswajeet Pradhan, Ketan Kotecha
2021 IEEE Access  
The findings show that using sophisticated input representation that includes traffic rules and road geometry, artificial intelligence-based solutions applied to behavior prediction of traffic actors for  ...  Accurate behavior prediction of surrounding traffic actors is essential for the safe and secure navigation of the intelligent vehicle.  ...  Long-term motion trajectory prediction is one activity where contextual cues become especially relevant.  ... 
doi:10.1109/access.2021.3116303 fatcat:spra4jjme5ezdpjhmn4uyptwva

Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving [article]

Junyao Guo, Unmesh Kurup, Mohak Shah
2018 arXiv   pre-print
models.  ...  both targeted dataset collection and the proposal of novel driveability metrics that enhance the robustness of autonomous cars in adverse environments.  ...  Maxim Likhachev from Carnegie Mellon University for his invaluable comments that improved the manuscript.  ... 
arXiv:1811.11277v1 fatcat:ztrxyydtuveijizfn6a2dmt5ui

Smart Urban Mobility: When Mobility Systems Meet Smart Data [article]

Zineb Mahrez, Essaid Sabir, Elarbi Badidi, Walid Saad, Mohamed Sadik
2020 arXiv   pre-print
The role of ITS is strengthened when combined with accurate artificial intelligence models that are built to optimize urban planning, analyze crowd behavior and predict traffic conditions.  ...  This article provides a survey of different approaches and technologies such as intelligent transportation systems (ITS) that leverage communication technologies to help maintain road users safe while  ...  More detailed ITS solutions designed for vulnerable road users are provided in Table I .  ... 
arXiv:2005.06626v1 fatcat:trlofmgej5c3hlkmezha373gca
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