5,118 research outputs found
Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections
Predicting the motion of a driver's vehicle is crucial for advanced driving
systems, enabling detection of potential risks towards shared control between
the driver and automation systems. In this paper, we propose a variational
neural network approach that predicts future driver trajectory distributions
for the vehicle based on multiple sensors. Our predictor generates both a
conditional variational distribution of future trajectories, as well as a
confidence estimate for different time horizons. Our approach allows us to
handle inherently uncertain situations, and reason about information gain from
each input, as well as combine our model with additional predictors, creating a
mixture of experts. We show how to augment the variational predictor with a
physics-based predictor, and based on their confidence estimations, improve
overall system performance. The resulting combined model is aware of the
uncertainty associated with its predictions, which can help the vehicle
autonomy to make decisions with more confidence. The model is validated on
real-world urban driving data collected in multiple locations. This validation
demonstrates that our approach improves the prediction error of a physics-based
model by 25% while successfully identifying the uncertain cases with 82%
accuracy.Comment: Accepted at ICRA'19. 8 pages, 9 figures, 1 table. Video at
https://youtu.be/clR08hRdtl
Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making
Tactical driving decision making is crucial for autonomous driving systems
and has attracted considerable interest in recent years. In this paper, we
propose several practical components that can speed up deep reinforcement
learning algorithms towards tactical decision making tasks: 1) non-uniform
action skipping as a more stable alternative to action-repetition frame
skipping, 2) a counter-based penalty for lanes on which ego vehicle has less
right-of-road, and 3) heuristic inference-time action masking for apparently
undesirable actions. We evaluate the proposed components in a realistic driving
simulator and compare them with several baselines. Results show that the
proposed scheme provides superior performance in terms of safety, efficiency,
and comfort.Comment: 7 pages, 2 figure
Machine Learning for Vehicular Networks
The emerging vehicular networks are expected to make everyday vehicular
operation safer, greener, and more efficient, and pave the path to autonomous
driving in the advent of the fifth generation (5G) cellular system. Machine
learning, as a major branch of artificial intelligence, has been recently
applied to wireless networks to provide a data-driven approach to solve
traditionally challenging problems. In this article, we review recent advances
in applying machine learning in vehicular networks and attempt to bring more
attention to this emerging area. After a brief overview of the major concept of
machine learning, we present some application examples of machine learning in
solving problems arising in vehicular networks. We finally discuss and
highlight several open issues that warrant further research.Comment: Accepted by IEEE Vehicular Technology Magazin
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
Connected and autonomous vehicles (CAVs) will form the backbone of future
next-generation intelligent transportation systems (ITS) providing travel
comfort, road safety, along with a number of value-added services. Such a
transformation---which will be fuelled by concomitant advances in technologies
for machine learning (ML) and wireless communications---will enable a future
vehicular ecosystem that is better featured and more efficient. However, there
are lurking security problems related to the use of ML in such a critical
setting where an incorrect ML decision may not only be a nuisance but can lead
to loss of precious lives. In this paper, we present an in-depth overview of
the various challenges associated with the application of ML in vehicular
networks. In addition, we formulate the ML pipeline of CAVs and present various
potential security issues associated with the adoption of ML methods. In
particular, we focus on the perspective of adversarial ML attacks on CAVs and
outline a solution to defend against adversarial attacks in multiple settings
Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture
Advanced Driver Assistance Systems (ADAS) have made driving safer over the
last decade. They prepare vehicles for unsafe road conditions and alert drivers
if they perform a dangerous maneuver. However, many accidents are unavoidable
because by the time drivers are alerted, it is already too late. Anticipating
maneuvers beforehand can alert drivers before they perform the maneuver and
also give ADAS more time to avoid or prepare for the danger.
In this work we propose a vehicular sensor-rich platform and learning
algorithms for maneuver anticipation. For this purpose we equip a car with
cameras, Global Positioning System (GPS), and a computing device to capture the
driving context from both inside and outside of the car. In order to anticipate
maneuvers, we propose a sensory-fusion deep learning architecture which jointly
learns to anticipate and fuse multiple sensory streams. Our architecture
consists of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory
(LSTM) units to capture long temporal dependencies. We propose a novel training
procedure which allows the network to predict the future given only a partial
temporal context. We introduce a diverse data set with 1180 miles of natural
freeway and city driving, and show that we can anticipate maneuvers 3.5 seconds
before they occur in real-time with a precision and recall of 90.5\% and 87.4\%
respectively.Comment: Journal Version (ICCV and ICRA combination with more system details)
http://brain4cars.co
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
GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
Despite the advancement in the technology of autonomous driving cars, the
safety of a self-driving car is still a challenging problem that has not been
well studied. Motion prediction is one of the core functions of an autonomous
driving car. Previously, we propose a novel scheme called GRIP which is
designed to predict trajectories for traffic agents around an autonomous car
efficiently. GRIP uses a graph to represent the interactions of close objects,
applies several graph convolutional blocks to extract features, and
subsequently uses an encoder-decoder long short-term memory (LSTM) model to
make predictions. Even though our experimental results show that GRIP improves
the prediction accuracy of the state-of-the-art solution by 30%, GRIP still has
some limitations. GRIP uses a fixed graph to describe the relationships between
different traffic agents and hence may suffer some performance degradations
when it is being used in urban traffic scenarios. Hence, in this paper, we
describe an improved scheme called GRIP++ where we use both fixed and dynamic
graphs for trajectory predictions of different types of traffic agents. Such an
improvement can help autonomous driving cars avoid many traffic accidents. Our
evaluations using a recently released urban traffic dataset, namely ApolloScape
showed that GRIP++ achieves better prediction accuracy than state-of-the-art
schemes. GRIP++ ranked #1 on the leaderboard of the ApolloScape trajectory
competition in October 2019. In addition, GRIP++ runs 21.7 times faster than a
state-of-the-art scheme, CS-LSTM
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
Reasoning about human motion is an important prerequisite to safe and
socially-aware robotic navigation. As a result, multi-agent behavior prediction
has become a core component of modern human-robot interactive systems, such as
self-driving cars. While there exist many methods for trajectory forecasting,
most do not enforce dynamic constraints and do not account for environmental
information (e.g., maps). Towards this end, we present Trajectron++, a modular,
graph-structured recurrent model that forecasts the trajectories of a general
number of diverse agents while incorporating agent dynamics and heterogeneous
data (e.g., semantic maps). Trajectron++ is designed to be tightly integrated
with robotic planning and control frameworks; for example, it can produce
predictions that are optionally conditioned on ego-agent motion plans. We
demonstrate its performance on several challenging real-world trajectory
forecasting datasets, outperforming a wide array of state-of-the-art
deterministic and generative methods.Comment: 23 pages, 6 figures, 5 tables. All code, models, and data can be
found at https://github.com/StanfordASL/Trajectron-plus-plus . European
Conference on Computer Vision (ECCV) 2020. Fixed a few typo
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007,
autonomous driving has been the most active field of AI applications. Almost at
the same time, deep learning has made breakthrough by several pioneers, three
of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won
ACM Turin Award in 2019. This is a survey of autonomous driving technologies
with deep learning methods. We investigate the major fields of self-driving
systems, such as perception, mapping and localization, prediction, planning and
control, simulation, V2X and safety etc. Due to the limited space, we focus the
analysis on several key areas, i.e. 2D and 3D object detection in perception,
depth estimation from cameras, multiple sensor fusion on the data, feature and
task level respectively, behavior modelling and prediction of vehicle driving
and pedestrian trajectories
A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior
We present a review and taxonomy of 200 models from the literature on driver
behavior modeling. We begin by introducing a mathematical framework for
describing the dynamics of interactive multi-agent traffic. Based on the
partially observable stochastic game, this framework provides a basis for
discussing different driver modeling techniques. Our taxonomy is constructed
around the core modeling tasks of state estimation, intention estimation, trait
estimation, and motion prediction, and also discusses the auxiliary tasks of
risk estimation, anomaly detection, behavior imitation and microscopic traffic
simulation. Existing driver models are categorized based on the specific tasks
they address and key attributes of their approach
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