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Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking
2020
IEEE transactions on intelligent transportation systems (Print)
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Unlike static obstacles, pedestrians are active agents with complex, interactive motions. ...
Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds ...
colliding with them. ...
doi:10.1109/tits.2020.3006768
fatcat:awa5dgk4rbazteetyyqrndbgxq
Pedestrian Models for Autonomous Driving Part I: low level models, from sensing to tracking
[article]
2020
arXiv
pre-print
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Unlike static and kinematic obstacles, pedestrians are active agents with complex, interactive motions. ...
Autonomous vehicles (AVs) must share space with human pedestrians, both in on-road cases such as cars at pedestrian crossings and off-road cases such as delivery vehicles navigating through crowds on high-streets ...
colliding with them. ...
arXiv:2002.11669v1
fatcat:fgg5j5jdwrbujjgtj2uhgrx2am
Problem Solving in Crowd Management Using Heuristic Approach
2022
IEEE Access
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The paper covers some of the previous works with similar approaches and presents state-of-the-art heuristic solutions for real-world problems. ...
Heuristic techniques are applied in many real-world problems, including crowd management; using heuristic-based models helped to comprehend crowd behavior better and increase simulation reliability. ...
The cost of adjusting body size is added into the function for optimal direction choice; with the different body size costs, the pedestrian will choose different directions. ...
doi:10.1109/access.2022.3156008
fatcat:a4qoutd4pjakle5n7pl3s6vwkm
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art
[article]
2021
arXiv
pre-print
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2019 pre-print arXiv:1704.05519v2 fatcat:vg2fezsgyzgptbpbvnwcpw24qyOther Versions
As with any rapidly growing field, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. ...
Infractions include driving on the opposite lane, driving on the sidewalk, colliding with other vehicles, colliding with pedestrians, and hitting static objects. Codevilla et al. ...
[119] use a combination of detection systems, each specialized in a different task such as pedestrian and upper body, face, skin color, depth-based shape, and motion. ...
arXiv:1704.05519v3
fatcat:xiintiarqjbfldheeg2hsydyra
Autonomous driving: cognitive construction and situation understanding
2019
Science China Information Sciences
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and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. ...
However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. ...
The key to deep reinforcement learning is to adjust the parameters of the deep network through the experience in the memory buffer to obtain the optimal strategy. Mnih et al. ...
doi:10.1007/s11432-018-9850-9
fatcat:qys3uucz3zgznfou6vgfjerwlq
Unfreezing autonomous vehicles with game theory, proxemics, and trust
2022
Frontiers in Computer Science
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This involves predicting and planning around pedestrians, understanding their personal space, and establishing trust with them. ...
But this highly safe nature may lead pedestrians to take advantage over them and slow their progress, even to a complete halt. ...
Neural network ("deep learning") based pedestrian detection and recognition has largely replaced classical feature-based methods, due to price falls in GPUs and new GPU-based heuristics enabling standard ...
doi:10.3389/fcomp.2022.969194
fatcat:fimssgkggzcnlbafu32ubqbrvy
Stochastic pedestrian models for autonomous vehicles
2022
figshare.com
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This dissertation addresses the modeling of pedestrians in dynamic and urban environments interacting with autonomous vehicles. ...
This work uses a new method that combines machine learning with reachability analysis (resulting in an adaptive funnel, hull, or belief set computation). ...
The car does not collide with the future pedestrian sets (walking pedestrian leads to dynamic constraints). ...
doi:10.6084/m9.figshare.21090139.v1
fatcat:5hb6w7ib4jchvjvlxsqfm4bzy4
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
[article]
2023
arXiv
pre-print
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Even more, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. ...
This work provides an overview of existing techniques and methods in the literature that combine data-driven models with existing knowledge. ...
An attention mechanism across channels is employed to represent various body parts. By emphasizing detected human body parts, occluded pedestrian detection results are improved. ...
arXiv:2205.04712v3
fatcat:saeka57uozelzowc4ql6uoe64a
Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques
2024
Journal of Big Data
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This paper offers an all-embracing survey of neural network-based methodologies for studying these driver bio-metrics, presenting an exhaustive examination of their advantages and drawbacks. ...
for audiences with diverse levels of understanding regarding the subject matter. ...
Belief Network -Back Propagation Neural Network ("DBN-BPNN") model. ...
doi:10.1186/s40537-024-00890-0
fatcat:oxt3rap74bhobdthnntf45xdde
Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice
[article]
2018
arXiv
pre-print
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In this literature review we aim to address the interaction problem between pedestrians and drivers (or vehicles) from joint attention point of view. ...
For instance, to deal with the problem of occlusion in the scene, Tian et al. [422] detect pedestrians based on their body parts. ...
In the first stage, the network only predicts part beliefs from local image evidence. ...
arXiv:1802.02522v2
fatcat:nzeq5eleajcktjl32m2kyqu7rq
Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning
2021
IEEE Access
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This work proposes using deep reinforcement (RL) learning as a framework to model the complex interactions and cooperation with nearby, decision-making agents, such as pedestrians and other robots. ...
The proposed algorithm is shown to outperform a classical collision avoidance algorithm, another deep RL-based algorithm, and scales with the number of agents better (fewer collisions, shorter time to ...
ACKNOWLEDGMENT Yu Fan Chen was with the Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA. ...
doi:10.1109/access.2021.3050338
fatcat:k54zg6mip5ganahre4jpianh3y
A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving
[article]
2019
arXiv
pre-print
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For tracking and prediction, approaches based on (deep) neural networks and other, especially stochastic techniques, are reported. ...
For decision making, deep reinforcement learning algorithms are presented, together with methods used to explore different alternative actions, such as Monte Carlo Tree Search. ...
Some authors handle this topic extensively, such as [29] which proposes a deep neural network for tracking occluded body parts, by processing features extracted from a VGG19 network. ...
arXiv:1909.07707v1
fatcat:h2ttehcuzrc2dnnmqzigilcyri
From Recognition to Prediction: Analysis of Human Action and Trajectory Prediction in Video
[article]
2021
arXiv
pre-print
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With the advancement in computer vision deep learning, systems now are able to analyze an unprecedented amount of rich visual information from videos to enable applications such as autonomous driving, ...
To enable optimal future human behavioral forecasting, it is crucial for the system to be able to detect and analyze human activities as well as scene semantics, passing informative features to the subsequent ...
Closest to ours is robust deep learning methods. ...
arXiv:2011.10670v3
fatcat:mlom5zqk6jdvjndcsfwimpj7xu
State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence
2018
Applied Sciences
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Third, we illustrate the artificial intelligence approach-especially deep learning methods-and discuss its combination with the estimation method. it is in relation to the surrounding environment. ...
The tracking algorithms are based mainly on estimation theory. Here, the target is a general one, either the robot itself or others. ...
This network and its improved version-deep belief network worked well in recognizing handwritten digits or for detecting pedestrians, especially when lack of sufficient labelled data [100] . ...
doi:10.3390/app8030379
fatcat:zx2u5ox4ivcvtm2vb2yg2kmbqm
Self-Driving Cars: A Survey
[article]
2019
arXiv
pre-print
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, traffic signalization detection and recognition, among others. ...
We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized ...
Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil, under Finance Code 001; Fundação de Amparoà Pesquisa do Espírito Santo (FAPES), Brazil, under Grant 84412844/2018; Vale company, Brazil, with ...
arXiv:1901.04407v2
fatcat:uwrgi5wjlbckdhtyy4eelinmde
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