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Multi-agent Interactive Prediction under Challenging Driving Scenarios [article]

Weihao Xuan, Ruijie Ren
2020 arXiv   pre-print
Moreover, the proposed multi-agent interactive prediction (MAIP) system is capable of simultaneously predicting any number of road entities while considering their mutual interactions.  ...  However, very few of them consider multi-agent prediction under challenging driving scenarios such as urban environment.  ...  [14] considers an urban area with 4way intersections by using HMM, and [15] uses Monte Carlo method to predict multimodal trajectories in urban intersections.  ... 
arXiv:1909.10737v4 fatcat:iehlyvzf7fgsfmirwtut56kzvq

Learning Interaction-Aware Probabilistic Driver Behavior Models from Urban Scenarios

Jens Schulz, Constantin Hubmann, Nikolai Morin, Julian Lochner, Darius Burschka
2019 2019 IEEE Intelligent Vehicles Symposium (IV)  
However, predicting complete trajectories at once is challenging, as one needs to account for multiple hypotheses and long-term interactions between multiple agents.  ...  Step-wise forward simulation of these models for the different possible routes of all agents allows for multi-modal and interaction-aware scene predictions at arbitrary road layouts.  ...  to next intersection d intersection whether always right of way at next intersection row always Interaction velocity of preceding agent V p v p distance to preceding agent V p d p velocity of  ... 
doi:10.1109/ivs.2019.8814080 dblp:conf/ivs/SchulzHMLB19 fatcat:vivggv7kercyhmzge4p3suwcoq

Incorporating Uncertainty in Predicting Vehicle Maneuvers at Intersections With Complex Interactions

Joonatan Manttari, John Folkesson
2019 2019 IEEE Intelligent Vehicles Symposium (IV)  
The work presented in this paper proposes a method of producing predictions of other traffic agents' trajectories in intersections with a singular Deep Learning module, while incorporating uncertainty  ...  The accuracy of the generated predictions is tested on a simulated intersection with a high level of interaction between agents, and different methods of incorporating uncertainty are compared.  ...  The work presented in this paper proposes a method of producing predictions of other traffic agents' trajectories in intersections with a singular Deep Learning module, while incorporating uncertainty  ... 
doi:10.1109/ivs.2019.8814159 dblp:conf/ivs/ManttariF19 fatcat:wxdqga6lhrdppmqpkh7pzbtqn4

SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs [article]

Sandra Carrasco, David Fernández Llorca, Miguel Ángel Sotelo
2021 arXiv   pre-print
The importance and influence of each interaction in the final prediction is explored by means of Integrated Gradients technique and the visualization of the attention learned.  ...  trajectories of vehicles and Vulnerable Road Users (VRUs) under mixed traffic conditions.  ...  and challenging type of traffic scenarios: urban unsignalized intersections and roundabouts.  ... 
arXiv:2102.06361v2 fatcat:cq5esusedfa5xi4g7hb7cjyruu

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

Noha Radwan, Wolfram Burgard, Abhinav Valada
2020 arXiv   pre-print
Our architecture consists of two subnetworks; an interaction-aware trajectory estimation stream IA-TCNN, that predicts the future states of all observed traffic participants in the scene, and a traffic  ...  Learned representations from the traffic light recognition stream are fused with the estimated trajectories from the motion prediction stream to learn the crossing decision.  ...  In order to learn a classifier that is robust to the type of intersection, feature maps from the traffic light recognition network and the interaction-aware motion prediction network are fused to learn  ... 
arXiv:1808.06887v5 fatcat:4hajrcyr6resvcvcxyykcmlppu

Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks [article]

Donsuk Lee, Yiming Gu, Jerrick Hoang, Micol Marchetti-Bowick
2019 arXiv   pre-print
In this work, we aim to predict the future motion of vehicles in a traffic scene by explicitly modeling their pairwise interactions.  ...  Finally, we show through simulation studies that the learned interaction modes are semantically meaningful.  ...  c) l ij = YIELDING if trajectories intersect and i arrives at the intersection point after j.  ... 
arXiv:1912.07882v1 fatcat:no6wxm2bvvg5nd36rhan6bijh4

Interaction-Aware Probabilistic Behavior Prediction in Urban Environments [article]

Jens Schulz, Constantin Hubmann, Julian Löchner, Darius Burschka
2018 arXiv   pre-print
Planning for autonomous driving in complex, urban scenarios requires accurate prediction of the trajectories of surrounding traffic participants.  ...  At first, the state of the dynamic Bayesian network is estimated over time by tracking the single agents via sequential Monte Carlo inference.  ...  Interaction-aware probabilistic trajectory prediction in an urban intersection scenario: the three vehicles have multiple possible routes, overlapping lanes and have to interact with each other.  ... 
arXiv:1804.10467v2 fatcat:h7zrskysovgzlg4fajdkhl4vei

Interaction-Aware Probabilistic Behavior Prediction in Urban Environments

Jens Schulz, Constantin Hubmann, Julian Lochner, Darius Burschka
2018 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
Planning for autonomous driving in complex, urban scenarios requires accurate prediction of the trajectories of surrounding traffic participants.  ...  At first, the state of the dynamic Bayesian network is estimated over time by tracking the single agents via sequential Monte Carlo inference.  ...  Interaction-aware probabilistic trajectory prediction in an urban intersection scenario: the three vehicles have multiple possible routes, overlapping lanes and have to interact with each other.  ... 
doi:10.1109/iros.2018.8594095 dblp:conf/iros/SchulzHLB18 fatcat:c7uulqkolzg7rgzjnw45omwy4m

Efficient and Safe Strategies for Intersection Management: A Review

Jian Wang, Xinyu Guo, Xinyu Yang
2021 Sensors  
Intersection management is a sophisticated event in the intelligent transportation system due to a variety of behavior for traffic participants.  ...  This paper primarily overviews recent studies on the scenes of intersection, aiming at improving the efficiency or guaranteeing the safety when vehicles pass the crossing.  ...  Louati et al. propose a method [70] that facilitates the passage of emergency vehicles through urban intersections. They rely on preemptive technology and multi-agent systems.  ... 
doi:10.3390/s21093096 pmid:33946781 fatcat:z7vx7e2ulzamrnoivvqvafthuq

TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents

Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, Dinesh Manocha
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.).  ...  Our approach uses an instance layer to learn instances' movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction.  ...  The novel components of our work include: • Propose a new approach for trajectory prediction in heterogeneous traffic. • Collect a new trajectory dataset in urban traffic with much interaction between  ... 
doi:10.1609/aaai.v33i01.33016120 fatcat:sheghpa6rrcmlfvcudcz55dory

TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents [article]

Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, Dinesh Manocha
2019 arXiv   pre-print
To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.).  ...  Our approach uses an instance layer to learn instances' movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction.  ...  The novel components of our work include: • Propose a new approach for trajectory prediction in heterogeneous traffic. • Collect a new trajectory dataset in urban traffic with much interaction between  ... 
arXiv:1811.02146v5 fatcat:zof5gnbzqbbwrhuxadoychkunq

Theory and Experiment of Cooperative Control at Multi-Intersections in Intelligent Connected Vehicle Environment: Review and Perspectives

Linan Zhang, Yizhe Wang, Huaizhong Zhu
2022 Sustainability  
A heterogeneous traffic flow consists of regular vehicles, and intelligent connected vehicles having interactive functions is updating the composition of the current urban-road network traffic flow.  ...  experimental validation in intelligent connected vehicles conditions, and intersection-oriented hybrid traffic control mechanism for urban road.  ...  At the beginning of the trajectory control section, the HV trajectory is predicted according to the entrance information of the upstream vehicle detector at the intersection [83] .  ... 
doi:10.3390/su14031542 fatcat:byylecdzbfayve3lleqbid2nq4

Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information [article]

A. Quintanar, D. Fernández-Llorca, I. Parra, R. Izquierdo, M. A. Sotelo
2021 arXiv   pre-print
Our model exploits these simple structures by adding augmented data (position and heading), and adapting their use to the problem of vehicle trajectory prediction in urban scenarios in prediction horizons  ...  Understanding the behavior of road users is of vital importance for the development of trajectory prediction systems.  ...  Future trajectories of the agents are then predicted by repeatedly sampling from the learned latent space. Most of the aforementioned approaches focused on pedestrian trajectories.  ... 
arXiv:2106.00559v2 fatcat:qofkflvrtnhgzigs2xcrqkmxiu

Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments [article]

Maxime Bouton, Alireza Nakhaei, Kikuo Fujimura, Mykel J. Kochenderfer
2019 arXiv   pre-print
Navigating urban environments represents a complex task for automated vehicles. They must reach their goal safely and efficiently while considering a multitude of traffic participants.  ...  We propose a modular decision making algorithm to autonomously navigate intersections, addressing challenges of existing rule-based and reinforcement learning (RL) approaches.  ...  Autonomously navigating urban intersections requires algorithms that reason about interactions between traffic participants with limited information.  ... 
arXiv:1904.11483v1 fatcat:niiykwcshfbbpeahvfflqmttpy

An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

Donghoon Shin, Subin Yi, Kang-moon Park, Manbok Park
2020 Applied Sciences  
It is demonstrated that the intention of a target vehicle is successfully predicted based on observations at an individual intersection by proposed algorithms.  ...  Research shows that urban intersections are a hotspot for traffic accidents which cause major human injuries.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app10062138 fatcat:mnic6yorbja6bev2s75bnpl7dm
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