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Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing [article]

Noha Radwan, Wolfram Burgard, Abhinav Valada
2020 arXiv   pre-print
In this paper, we address this challenge and propose a multimodal convolutional neural network framework to predict the safety of a street intersection for crossing.  ...  For mobile robots navigating on sidewalks, it is essential to be able to safely cross street intersections.  ...  Conclusion In this paper, we proposed a system for autonomous street crossing using multimodal data.  ... 
arXiv:1808.06887v5 fatcat:4hajrcyr6resvcvcxyykcmlppu

Multimodal Hybrid Pedestrian: A hybrid automaton model of urban pedestrian behavior for automated driving applications

Suresh Kumaar Jayaraman, Lionel P. Robert, X. Jessie Yang, Dawn M. Tilbury
2021 IEEE Access  
The MHP model is applicable to a wide variety of urban scenarios-midblocks, intersections, one-way, and two-way streets, etc., and the probabilistic predictions from the model can be utilized for AV motion  ...  We account for multimodal pedestrian behavior by identifying pedestrian decision-making points and developing decision-making models to predict pedestrian behaviors in a probabilistic hybrid automaton  ...  CROSSING DECISION MODEL To safely interact with crossing pedestrians, the AVs should be able to predict whether and when pedestrians will cross the street.  ... 
doi:10.1109/access.2021.3058307 fatcat:sur5o6dcxzbolozmk2jpqsxv6q

Towards a Human-Centred Cognitive Model of Visuospatial Complexity in Everyday Driving [article]

Vasiliki Kondyli and Mehul Bhatt and Jakob Suchan
2020 arXiv   pre-print
We report preliminary steps to apply the developed cognitive model of visuospatial complexity for human-factors guided dataset creation and benchmarking, and for its use as a semantic template for the  ...  It is a typical scenario of pedestrian on a low traffic street with no official indication for crossing such as zebra crossing or traffic light.  ...  The central focus of the research presented in this paper is to develop a systematic methodology for the development of human-centred benchmarks for visual sensemaking and multimodal interaction for the  ... 
arXiv:2006.00059v2 fatcat:mepqaecucbfyhdf52ftc2mwnve

Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior [article]

Osama Makansi, Özgün Cicek, Kevin Buchicchio, Thomas Brox
2020 arXiv   pre-print
Experiments show that the reachability prior combined with multi-hypotheses learning improves multimodal prediction of the future location of tracked objects and, for the first time, the emergence of new  ...  We rather estimate a reachability prior for certain classes of objects from the semantic map of the present image and propagate it into the future using the planned egomotion.  ...  [63] added the planned egomotion to further improve the prediction. For autonomous driving, knowing the planned motion is a reasonable assumption [20] , and we also make use of this assumption.  ... 
arXiv:2006.04700v1 fatcat:2arah4qiybdaro63tykxw24izy

Dynamic and Static Context-aware LSTM for Multi-agent Motion Prediction [article]

Chaofan Tao, Qinhong Jiang, Lixin Duan, Ping Luo
2020 arXiv   pre-print
However, unlike previous work that isolated the spatial interaction, temporal coherence, and scene layout, this paper designs a new mechanism, i.e., Dynamic and Static Context-aware Motion Predictor (DSCMP  ...  Multi-agent motion prediction is challenging because it aims to foresee the future trajectories of multiple agents (e.g. pedestrians) simultaneously in a complicated scene.  ...  Introduction Multi-agent motion prediction is an important task for many real-world applications such as self-driving vehicle, traffic surveillance, and autonomous mobile robot.  ... 
arXiv:2008.00777v1 fatcat:csrb27sxhzghzjvinvo2lucjhm

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
Recent advances in trajectory prediction have shown that explicit reasoning about agents' intent is important to accurately forecast their motion.  ...  and vehicles) in an autonomous driving setting.  ...  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

MultiXNet: Multiclass Multistage Multimodal Motion Prediction [article]

Nemanja Djuric, Henggang Cui, Zhaoen Su, Shangxuan Wu, Huahua Wang, Fang-Chieh Chou, Luisa San Martin, Song Feng, Rui Hu, Yang Xu, Alyssa Dayan, Sidney Zhang (+4 others)
2021 arXiv   pre-print
To address this task we propose MultiXNet, an end-to-end approach for detection and motion prediction based directly on lidar sensor data.  ...  over future actor motion that includes both multiple discrete traffic behaviors and calibrated continuous position uncertainties.  ...  performance. 1) Uncertainty-aware loss: In addition to predicting trajectories, an important task in autonomous driving is the estimation of their spatial uncertainty.  ... 
arXiv:2006.02000v4 fatcat:ggn5l4yngjdexms7zbwgtire3a

Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation [article]

Patrick Dendorfer and Aljoša Ošep and Laura Leal-Taixé
2020 arXiv   pre-print
In this paper, we present Goal-GAN, an interpretable and end-to-end trainable model for human trajectory prediction.  ...  Inspired by human navigation, we model the task of trajectory prediction as an intuitive two-stage process: (i) goal estimation, which predicts the most likely target positions of the agent, followed by  ...  Introduction Modeling human motion is indispensable for autonomous systems that operate in public spaces, such as self-driving cars or social robots.  ... 
arXiv:2010.01114v1 fatcat:mskcix3vdfcfncmlr5cmlkcseu

Control-Aware Prediction Objectives for Autonomous Driving [article]

Rowan McAllister, Blake Wulfe, Jean Mercat, Logan Ellis, Sergey Levine, Adrien Gaidon
2022 arXiv   pre-print
In this paper, we present control-aware prediction objectives (CAPOs), to evaluate the downstream effect of predictions on control without requiring the planner be differentiable.  ...  Autonomous vehicle software is typically structured as a modular pipeline of individual components (e.g., perception, prediction, and planning) to help separate concerns into interpretable sub-tasks.  ...  Occasionally, a pedestrian will cross the street and the ego agent must slow to avoid a collision when necessary.  ... 
arXiv:2204.13319v1 fatcat:tt4qtmmyxzg4tbp26dn3scz3fu

Conditional Generative Adversarial Networks for Speed Control in Trajectory Simulation [article]

Sahib Julka, Vishal Sowrirajan, Joerg Schloetterer, Michael Granitzer
2021 arXiv   pre-print
Prediction of motion, in application, must be realistic, diverse and controllable.  ...  In spite of increasing focus on multimodal trajectory generation, most methods still lack means for explicitly controlling different modes of the data generation.  ...  Introduction Modelling social interactions and the ability to forecast motion dynamics is pertinent to several application domains such as robot planning systems [1] , traffic operations [2] , and autonomous  ... 
arXiv:2103.11471v1 fatcat:o2o5uddd4ndwjazdqva2ykvusa

Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies [article]

Yu Huang, Yue Chen
2020 arXiv   pre-print
This is a survey of autonomous driving technologies with deep learning methods.  ...  Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications.  ...  In 2007, DAPRA also held the Urban Challenges for autonomous driving in street environments. Then professor S.  ... 
arXiv:2006.06091v3 fatcat:nhdgivmtrzcarp463xzqvnxlwq

A Computationally Efficient Model for Pedestrian Motion Prediction

Ivo Batkovic, Mario Zanon, Nils Lubbe, Paolo Falcone
2018 2018 European Control Conference (ECC)  
We present a mathematical model to predict pedestrian motion over a finite horizon, intended for use in collision avoidance algorithms for autonomous driving.  ...  The dataset consists of 46 trajectories: 29 crossing the street and 17 remain on the side walks. A.  ...  Because our method is not designed to predict such situations, it predicts that the pedestrian will cross the road closer to the zebra crossing.  ... 
doi:10.23919/ecc.2018.8550300 dblp:conf/eucc/BatkovicZLF18 fatcat:tnoki5qlyjhp5prr6sq64cuiau

A Computationally Efficient Model for Pedestrian Motion Prediction [article]

Ivo Batkovic, Mario Zanon, Nils Lubbe, Paolo Falcone
2018 arXiv   pre-print
We present a mathematical model to predict pedestrian motion over a finite horizon, intended for use in collision avoidance algorithms for autonomous driving.  ...  The dataset consists of 46 trajectories: 29 crossing the street and 17 remain on the side walks. A.  ...  Because our method is not designed to predict such situations, it predicts that the pedestrian will cross the road closer to the zebra crossing.  ... 
arXiv:1803.04702v1 fatcat:zyj6romcqrbcnglcrolgvi65uy

Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving [article]

Nemanja Djuric, Vladan Radosavljevic, Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, Nitin Singh, Jeff Schneider
2020 arXiv   pre-print
We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings.  ...  The rasters are then used as inputs to deep convolutional models to infer future movement of actors while also accounting for and capturing inherent uncertainty of the prediction task.  ...  Results In Table 1 we report error metrics relevant for motion prediction: displacement errors, as well as along-track and cross-track errors [15] , averaged over the prediction horizon.  ... 
arXiv:1808.05819v3 fatcat:sojqsx46cbccvouzzisuq7xg6y

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
This paper provides a survey of human motion trajectory prediction.  ...  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
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