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Multi-agent Interactive Prediction under Challenging Driving Scenarios
[article]
2020
arXiv
pre-print
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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
2019
2019 IEEE Intelligent Vehicles Symposium (IV)
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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
2019
2019 IEEE Intelligent Vehicles Symposium (IV)
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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2020 pre-print arXiv:1808.06887v4 fatcat:5uwjomh2vvbxtcrpsxx4xjoa5yOther Versions
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]
2019
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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
2018
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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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
2021
Sensors
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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
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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]
2019
arXiv
pre-print
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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
2022
Sustainability
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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]
2021
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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
2020
Applied Sciences
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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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