109 research outputs found
Intent prediction of vulnerable road users for trusted autonomous vehicles
This study investigated how future autonomous vehicles could be further trusted by vulnerable road users (such as pedestrians and cyclists) that they would be interacting with in urban traffic environments. It focused on understanding the behaviours of such road users on a deeper level by predicting their future intentions based solely on vehicle-based sensors and AI techniques. The findings showed that personal/body language attributes of vulnerable road users besides their past motion trajectories and physics attributes in the environment led to more accurate predictions about their intended actions
Local and Global Contextual Features Fusion for Pedestrian Intention Prediction
Autonomous vehicles (AVs) are becoming an indispensable part of future
transportation. However, safety challenges and lack of reliability limit their
real-world deployment. Towards boosting the appearance of AVs on the roads, the
interaction of AVs with pedestrians including "prediction of the pedestrian
crossing intention" deserves extensive research. This is a highly challenging
task as involves multiple non-linear parameters. In this direction, we extract
and analyse spatio-temporal visual features of both pedestrian and traffic
contexts. The pedestrian features include body pose and local context features
that represent the pedestrian's behaviour. Additionally, to understand the
global context, we utilise location, motion, and environmental information
using scene parsing technology that represents the pedestrian's surroundings,
and may affect the pedestrian's intention. Finally, these multi-modality
features are intelligently fused for effective intention prediction learning.
The experimental results of the proposed model on the JAAD dataset show a
superior result on the combined AUC and F1-score compared to the
state-of-the-art
Vulnerable road users and connected autonomous vehicles interaction: a survey
There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.This work was partially funded by the Ministry of Economy, Industry, and Competitiveness
of Spain under Grant: Supervision of drone fleet and optimization of commercial operations flight
plans, PID2020-116377RB-C21.Peer ReviewedPostprint (published version
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Pedestrian and vehicle behaviour prediction in autonomous vehicle system — A review
Data availability: No data was used for the research described in the article.Copyright © 2023 The Author(s). Autonomous vehicles (AV)s have become a trending topic nowadays since they have the potential to solve traffic problems, such as accidents and congestion. Although AV systems have greatly evolved, it still have their limitations. For example, Google reported that their AVs have been involved in several collisions and near misses. While most of these collisions and near misses were caused by third parties, the AVs should be able to predict and avoid them. Events like this show that there is still room for improvement in the AV system. This paper aims to present a review of the state-of-the-art algorithms proposed to enable AV behaviour prediction systems to predict trajectories and intentions for pedestrians and vehicles. This will be achieved by using information from previous literature review papers, recent works, and results obtained using well-known datasets.EPSRC DTP Ph.D. studentship at Brunel University London, United Kingdom
Pedestrian and cyclist detection and intent estimation for autonomous vehicles: A survey
© 2019 by the authors. As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN), Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated
Deep Virtual-to-Real Distillation for Pedestrian Crossing Prediction
Pedestrian crossing is one of the most typical behavior which conflicts with
natural driving behavior of vehicles. Consequently, pedestrian crossing
prediction is one of the primary task that influences the vehicle planning for
safe driving. However, current methods that rely on the practically collected
data in real driving scenes cannot depict and cover all kinds of scene
condition in real traffic world. To this end, we formulate a deep virtual to
real distillation framework by introducing the synthetic data that can be
generated conveniently, and borrow the abundant information of pedestrian
movement in synthetic videos for the pedestrian crossing prediction in real
data with a simple and lightweight implementation. In order to verify this
framework, we construct a benchmark with 4667 virtual videos owning about 745k
frames (called Virtual-PedCross-4667), and evaluate the proposed method on two
challenging datasets collected in real driving situations, i.e., JAAD and PIE
datasets. State-of-the-art performance of this framework is demonstrated by
exhaustive experiment analysis. The dataset and code can be downloaded from the
website \url{http://www.lotvs.net/code_data/}.Comment: Accepted by ITSC 202
Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review
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. This helps enhance their awareness of the imminent
hazards. However, conventional behaviour prediction solutions are applicable in
simple driving scenarios that require short prediction horizons. Most recently,
deep learning-based approaches have become popular due to their superior
performance in more complex environments compared to the conventional
approaches. 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. We firstly give an overview of the generic
problem of vehicle behaviour prediction and discuss its challenges, followed by
classification and review of the most recent deep learning-based solutions
based on three criteria: input representation, output type, and prediction
method. The paper also discusses the performance of several well-known
solutions, identifies the research gaps in the literature and outlines
potential new research directions
Deep learning-based vehicle behaviour prediction for autonomous driving applications : a review
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. This helps enhance their awareness of the imminent hazards. However, conventional behavior prediction solutions are applicable in simple driving scenarios that require short prediction horizons. Most recently, deep learning-based approaches have become popular due to their promising performance in more complex environments compared to the conventional approaches. Motivated by this increased popularity, we provide a comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behavior prediction in this article. We firstly give an overview of the generic problem of vehicle behavior prediction and discuss its challenges, followed by classification and review of the most recent deep learning-based solutions based on three criteria: input representation, output type, and prediction method. The article also discusses the performance of several well-known solutions, identifies the research gaps in the literature and outlines potential new research directions
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