15,803 research outputs found
Pedestrian Path, Pose and Intention Prediction through Gaussian Process Dynamical Models and Pedestrian Activity Recognition
According to several reports published by worldwide organisations, thousands
of pedestrians die in road accidents every year. Due to this fact, vehicular
technologies have been evolving with the intent of reducing these fatalities.
This evolution has not finished yet since, for instance, the predictions of
pedestrian paths could improve the current Automatic Emergency Braking Systems
(AEBS). For this reason, this paper proposes a method to predict future
pedestrian paths, poses and intentions up to 1s in advance. This method is
based on Balanced Gaussian Process Dynamical Models (B-GPDMs), which reduce the
3D time-related information extracted from keypoints or joints placed along
pedestrian bodies into low-dimensional spaces. The B-GPDM is also capable of
inferring future latent positions and reconstruct their associated
observations. However, learning a generic model for all kind of pedestrian
activities normally provides less ccurate predictions. For this reason, the
proposed method obtains multiple models of four types of activity, i.e.
walking, stopping, starting and standing, and selects the most similar model to
estimate future pedestrian states. This method detects starting activities
125ms after the gait initiation with an accuracy of 80% and recognises stopping
intentions 58.33ms before the event with an accuracy of 70%. Concerning the
path prediction, the mean error for stopping activities at a Time-To-Event
(TTE) of 1s is 238.01mm and, for starting actions, the mean error at a TTE of
0s is 331.93mm.Comment: 12 page
A Real-time Vision Framework for Pedestrian Behavior Recognition and Intention Prediction at Intersections Using 3D Pose Estimation
Minimizing traffic accidents between vehicles and pedestrians is one of the
primary research goals in intelligent transportation systems. To achieve the
goal, pedestrian behavior recognition and prediction of pedestrian's crossing
or not-crossing intention play a central role. Contemporary approaches do not
guarantee satisfactory performance due to lack of generalization, the
requirement of manual data labeling, and high computational complexity. To
overcome these limitations, we propose a real-time vision framework for two
tasks: pedestrian behavior recognition (100.53 FPS) and intention prediction
(35.76 FPS). Our framework obtains satisfying generalization over multiple
sites because of the proposed site-independent features. At the center of the
feature extraction lies 3D pose estimation. The 3D pose analysis enables robust
and accurate recognition of pedestrian behaviors and prediction of intentions
over multiple sites. The proposed vision framework realizes 89.3% accuracy in
the behavior recognition task on the TUD dataset without any training process
and 91.28% accuracy in intention prediction on our dataset achieving new
state-of-the-art performance. To contribute to the corresponding research
community, we make our source codes public which are available at
https://github.com/Uehwan/VisionForPedestrianComment: 12 pages, 6 figures, 4 table
Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice
Today, one of the major challenges that autonomous vehicles are facing is the
ability to drive in urban environments. Such a task requires communication
between autonomous vehicles and other road users in order to resolve various
traffic ambiguities. The interaction between road users is a form of
negotiation in which the parties involved have to share their attention
regarding a common objective or a goal (e.g. crossing an intersection), and
coordinate their actions in order to accomplish it. In this literature review
we aim to address the interaction problem between pedestrians and drivers (or
vehicles) from joint attention point of view. More specifically, we will
discuss the theoretical background behind joint attention, its application to
traffic interaction and practical approaches to implementing joint attention
for autonomous vehicles
Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning
Avoiding collisions with vulnerable road users (VRUs) using sensor-based
early recognition of critical situations is one of the manifold opportunities
provided by the current development in the field of intelligent vehicles. As
especially pedestrians and cyclists are very agile and have a variety of
movement options, modeling their behavior in traffic scenes is a challenging
task. In this article we propose movement models based on machine learning
methods, in particular artificial neural networks, in order to classify the
current motion state and to predict the future trajectory of VRUs. Both model
types are also combined to enable the application of specifically trained
motion predictors based on a continuously updated pseudo probabilistic state
classification. Furthermore, the architecture is used to evaluate
motion-specific physical models for starting and stopping and video-based
pedestrian motion classification. A comprehensive dataset consisting of 1068
pedestrian and 494 cyclist scenes acquired at an urban intersection is used for
optimization, training, and evaluation of the different models. The results
show substantial higher classification rates and the ability to earlier
recognize motion state changes with the machine learning approaches compared to
interacting multiple model (IMM) Kalman Filtering. The trajectory prediction
quality is also improved for all kinds of test scenes, especially when starting
and stopping motions are included. Here, 37\% and 41\% lower position errors
were achieved on average, respectively
Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis
Pedestrians and vehicles often share the road in complex inner city traffic.
This leads to interactions between the vehicle and pedestrians, with each
affecting the other's motion. In order to create robust methods to reason about
pedestrian behavior and to design interfaces of communication between
self-driving cars and pedestrians we need to better understand such
interactions. In this paper, we present a data-driven approach to implicitly
model pedestrians' interactions with vehicles, to better predict pedestrian
behavior. We propose a LSTM model that takes as input the past trajectories of
the pedestrian and ego-vehicle, and pedestrian head orientation, and predicts
the future positions of the pedestrian. Our experiments based on a real-world,
inner city dataset captured with vehicle mounted cameras, show that the usage
of such cues improve pedestrian prediction when compared to a baseline that
purely uses the past trajectory of the pedestrian.Comment: IV 201
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving
With recent advances in learning algorithms and hardware development,
autonomous cars have shown promise when operating in structured environments
under good driving conditions. However, for complex, cluttered and unseen
environments with high uncertainty, autonomous driving systems still frequently
demonstrate erroneous or unexpected behaviors, that could lead to catastrophic
outcomes. Autonomous vehicles should ideally adapt to driving conditions; while
this can be achieved through multiple routes, it would be beneficial as a first
step to be able to characterize Driveability in some quantified form. To this
end, this paper aims to create a framework for investigating different factors
that can impact driveability. Also, one of the main mechanisms to adapt
autonomous driving systems to any driving condition is to be able to learn and
generalize from representative scenarios. The machine learning algorithms that
currently do so learn predominantly in a supervised manner and consequently
need sufficient data for robust and efficient learning. Therefore, we also
perform a comparative overview of 45 public driving datasets that enable
learning and publish this dataset index at
https://sites.google.com/view/driveability-survey-datasets. Specifically, we
categorize the datasets according to use cases, and highlight the datasets that
capture complicated and hazardous driving conditions which can be better used
for training robust driving models. Furthermore, by discussions of what driving
scenarios are not covered by existing public datasets and what driveability
factors need more investigation and data acquisition, this paper aims to
encourage both targeted dataset collection and the proposal of novel
driveability metrics that enhance the robustness of autonomous cars in adverse
environments
Learning to Detect Vehicles by Clustering Appearance Patterns
This paper studies efficient means for dealing with intra-category diversity
in object detection. Strategies for occlusion and orientation handling are
explored by learning an ensemble of detection models from visual and
geometrical clusters of object instances. An AdaBoost detection scheme is
employed with pixel lookup features for fast detection. The analysis provides
insight into the design of a robust vehicle detection system, showing promise
in terms of detection performance and orientation estimation accuracy.Comment: Preprint version of our T-ITS 2015 pape
Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction
Anomaly detection from a driver's perspective when driving is important to
autonomous vehicles. As a part of Advanced Driver Assistance Systems (ADAS), it
can remind the driver about dangers timely. Compared with traditional studied
scenes such as the university campus and market surveillance videos, it is
difficult to detect abnormal event from a driver's perspective due to camera
waggle, abidingly moving background, drastic change of vehicle velocity, etc.
To tackle these specific problems, this paper proposes a spatial localization
constrained sparse coding approach for anomaly detection in traffic scenes,
which firstly measures the abnormality of motion orientation and magnitude
respectively and then fuses these two aspects to obtain a robust detection
result. The main contributions are threefold: 1) This work describes the motion
orientation and magnitude of the object respectively in a new way, which is
demonstrated to be better than the traditional motion descriptors. 2) The
spatial localization of object is taken into account of the sparse
reconstruction framework, which utilizes the scene's structural information and
outperforms the conventional sparse coding methods. 3) Results of motion
orientation and magnitude are adaptively weighted and fused by a Bayesian
model, which makes the proposed method more robust and handle more kinds of
abnormal events. The efficiency and effectiveness of the proposed method are
validated by testing on nine difficult video sequences captured by ourselves.
Observed from the experimental results, the proposed method is more effective
and efficient than the popular competitors, and yields a higher performance.Comment: IEEE Transactions on Intelligent Transportation System
Context-Aware Pedestrian Motion Prediction In Urban Intersections
This paper presents a novel context-based approach for pedestrian motion
prediction in crowded, urban intersections, with the additional flexibility of
prediction in similar, but new, environments. Previously, Chen et. al. combined
Markovian-based and clustering-based approaches to learn motion primitives in a
grid-based world and subsequently predict pedestrian trajectories by modeling
the transition between learned primitives as a Gaussian Process (GP). This work
extends that prior approach by incorporating semantic features from the
environment (relative distance to curbside and status of pedestrian traffic
lights) in the GP formulation for more accurate predictions of pedestrian
trajectories over the same timescale. We evaluate the new approach on
real-world data collected using one of the vehicles in the MIT Mobility On
Demand fleet. The results show 12.5% improvement in prediction accuracy and a
2.65 times reduction in Area Under the Curve (AUC), which is used as a metric
to quantify the span of predicted set of trajectories, such that a lower AUC
corresponds to a higher level of confidence in the future direction of
pedestrian motion
Vulnerable road user detection: state-of-the-art and open challenges
Correctly identifying vulnerable road users (VRUs), e.g. cyclists and
pedestrians, remains one of the most challenging environment perception tasks
for autonomous vehicles (AVs). This work surveys the current state-of-the-art
in VRU detection, covering topics such as benchmarks and datasets, object
detection techniques and relevant machine learning algorithms. The article
concludes with a discussion of remaining open challenges and promising future
research directions for this domain
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