14,255 research outputs found
Identifying Modes of Intent from Driver Behaviors in Dynamic Environments
In light of growing attention of intelligent vehicle systems, we propose
developing a driver model that uses a hybrid system formulation to capture the
intent of the driver. This model hopes to capture human driving behavior in a
way that can be utilized by semi- and fully autonomous systems in heterogeneous
environments. We consider a discrete set of high level goals or intent modes,
that is designed to encompass the decision making process of the human. A
driver model is derived using a dataset of lane changes collected in a
realistic driving simulator, in which the driver actively labels data to give
us insight into her intent. By building the labeled dataset, we are able to
utilize classification tools to build the driver model using features of based
on her perception of the environment, and achieve high accuracy in identifying
driver intent. Multiple algorithms are presented and compared on the dataset,
and a comparison of the varying behaviors between drivers is drawn. Using this
modeling methodology, we present a model that can be used to assess driver
behaviors and to develop human-inspired safety metrics that can be utilized in
intelligent vehicular systems.Comment: Submitted to ITSC 201
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles
Perception technologies in Autonomous Driving are experiencing their golden
age due to the advances in Deep Learning. Yet, most of these systems rely on
the semantically rich information of RGB images. Deep Learning solutions
applied to the data of other sensors typically mounted on autonomous cars (e.g.
lidars or radars) are not explored much. In this paper we propose a novel
solution to understand the dynamics of moving vehicles of the scene from only
lidar information. The main challenge of this problem stems from the fact that
we need to disambiguate the proprio-motion of the 'observer' vehicle from that
of the external 'observed' vehicles. For this purpose, we devise a CNN
architecture which at testing time is fed with pairs of consecutive lidar
scans. However, in order to properly learn the parameters of this network,
during training we introduce a series of so-called pretext tasks which also
leverage on image data. These tasks include semantic information about
vehicleness and a novel lidar-flow feature which combines standard image-based
optical flow with lidar scans. We obtain very promising results and show that
including distilled image information only during training, allows improving
the inference results of the network at test time, even when image data is no
longer used.Comment: Presented in IEEE ICRA 2018. IEEE Copyrights: Personal use of this
material is permitted. Permission from IEEE must be obtained for all other
uses. (V2 just corrected comments on arxiv submission
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation
Our recent work suggests that, thanks to nowadays powerful CNNs, image-based
2D pose estimation is a promising cue for determining pedestrian intentions
such as crossing the road in the path of the ego-vehicle, stopping before
entering the road, and starting to walk or bending towards the road. This
statement is based on the results obtained on non-naturalistic sequences
(Daimler dataset), i.e. in sequences choreographed specifically for performing
the study. Fortunately, a new publicly available dataset (JAAD) has appeared
recently to allow developing methods for detecting pedestrian intentions in
naturalistic driving conditions; more specifically, for addressing the relevant
question is the pedestrian going to cross? Accordingly, in this paper we use
JAAD to assess the usefulness of 2D pose estimation for answering such a
question. We combine CNN-based pedestrian detection, tracking and pose
estimation to predict the crossing action from monocular images. Overall, the
proposed pipeline provides new state-of-the-art results.Comment: This is a paper presented in IEEE Intelligent Vehicles Symposium
(IEEE IV 2018
Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance
Due to the current developments towards autonomous driving and vehicle active
safety, there is an increasing necessity for algorithms that are able to
perform complex criticality predictions in real-time. Being able to process
multi-object traffic scenarios aids the implementation of a variety of
automotive applications such as driver assistance systems for collision
prevention and mitigation as well as fall-back systems for autonomous vehicles.
We present a fully model-based algorithm with a parallelizable architecture.
The proposed algorithm can evaluate the criticality of complex, multi-modal
(vehicles and pedestrians) traffic scenarios by simulating millions of
trajectory combinations and detecting collisions between objects. The algorithm
is able to estimate upcoming criticality at very early stages, demonstrating
its potential for vehicle safety-systems and autonomous driving applications.
An implementation on an embedded system in a test vehicle proves in a
prototypical manner the compatibility of the algorithm with the hardware
possibilities of modern cars. For a complex traffic scenario with 11 dynamic
objects, more than 86 million pose combinations are evaluated in 21 ms on the
GPU of a Drive PX~2
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Predicting the future location of vehicles is essential for safety-critical
applications such as advanced driver assistance systems (ADAS) and autonomous
driving. This paper introduces a novel approach to simultaneously predict both
the location and scale of target vehicles in the first-person (egocentric) view
of an ego-vehicle. We present a multi-stream recurrent neural network (RNN)
encoder-decoder model that separately captures both object location and scale
and pixel-level observations for future vehicle localization. We show that
incorporating dense optical flow improves prediction results significantly
since it captures information about motion as well as appearance change. We
also find that explicitly modeling future motion of the ego-vehicle improves
the prediction accuracy, which could be especially beneficial in intelligent
and automated vehicles that have motion planning capability. To evaluate the
performance of our approach, we present a new dataset of first-person videos
collected from a variety of scenarios at road intersections, which are
particularly challenging moments for prediction because vehicle trajectories
are diverse and dynamic.Comment: To appear on ICRA 201
FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network
Pedestrian intention recognition is very important to develop robust and safe
autonomous driving (AD) and advanced driver assistance systems (ADAS)
functionalities for urban driving. In this work, we develop an end-to-end
pedestrian intention framework that performs well on day- and night- time
scenarios. Our framework relies on objection detection bounding boxes combined
with skeletal features of human pose. We study early, late, and combined (early
and late) fusion mechanisms to exploit the skeletal features and reduce false
positives as well to improve the intention prediction performance. The early
fusion mechanism results in AP of 0.89 and precision/recall of 0.79/0.89 for
pedestrian intention classification. Furthermore, we propose three new metrics
to properly evaluate the pedestrian intention systems. Under these new
evaluation metrics for the intention prediction, the proposed end-to-end
network offers accurate pedestrian intention up to half a second ahead of the
actual risky maneuver.Comment: 5 pages, 6 figures, 5 tables, IEEE Asilomar SS
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