23 research outputs found
Controlling Steering Angle for Cooperative Self-driving Vehicles utilizing CNN and LSTM-based Deep Networks
A fundamental challenge in autonomous vehicles is adjusting the steering
angle at different road conditions. Recent state-of-the-art solutions
addressing this challenge include deep learning techniques as they provide
end-to-end solution to predict steering angles directly from the raw input
images with higher accuracy. Most of these works ignore the temporal
dependencies between the image frames. In this paper, we tackle the problem of
utilizing multiple sets of images shared between two autonomous vehicles to
improve the accuracy of controlling the steering angle by considering the
temporal dependencies between the image frames. This problem has not been
studied in the literature widely. We present and study a new deep architecture
to predict the steering angle automatically by using Long-Short-Term-Memory
(LSTM) in our deep architecture. Our deep architecture is an end-to-end network
that utilizes CNN, LSTM and fully connected (FC) layers and it uses both
present and futures images (shared by a vehicle ahead via Vehicle-to-Vehicle
(V2V) communication) as input to control the steering angle. Our model
demonstrates the lowest error when compared to the other existing approaches in
the literature.Comment: Accepted in IV 2019, 6 pages, 9 figure
Control-aware Communication for Cooperative Adaptive Cruise Control
Utilizing vehicle-to-everything (V2X) communication technologies, vehicle
platooning systems are expected to realize a new paradigm of cooperative
driving with higher levels of traffic safety and efficiency. Connected and
Autonomous Vehicles (CAVs) need to have proper awareness of the traffic
context. However, as the quantity of interconnected entities grows, the expense
of communication will become a significant factor. As a result, the cooperative
platoon's performance will be influenced by the communication strategy. While
maintaining desired levels of performance, periodic communication can be
relaxed to more flexible aperiodic or event-triggered implementations. In this
paper, we propose a control-aware communication solution for vehicle platoons.
The method uses a fully distributed control-aware communication strategy,
attempting to decrease the usage of communication resources while still
preserving the desired closed-loop performance characteristics. We then
leverage Model-Based Communication (MBC) to improve cooperative vehicle
perception in non-ideal communication and propose a solution that combines
control-aware communication with MBC for cooperative control of vehicle
platoons. Our approach achieves a significant reduction in the average
communication rate () while only slightly reducing control performance
(e.g., less than speed deviation). Through extensive simulations, we
demonstrate the benefits of combined control-aware communication with MBC for
cooperative control of vehicle platoons.Comment: arXiv admin note: text overlap with arXiv:2203.1577
Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
It is expected that autonomous vehicles(AVs) and heterogeneous human-driven
vehicles(HVs) will coexist on the same road. The safety and reliability of AVs
will depend on their social awareness and their ability to engage in complex
social interactions in a socially accepted manner. However, AVs are still
inefficient in terms of cooperating with HVs and struggle to understand and
adapt to human behavior, which is particularly challenging in mixed autonomy.
In a road shared by AVs and HVs, the social preferences or individual traits of
HVs are unknown to the AVs and different from AVs, which are expected to follow
a policy, HVs are particularly difficult to forecast since they do not
necessarily follow a stationary policy. To address these challenges, we frame
the mixed-autonomy problem as a multi-agent reinforcement learning (MARL)
problem and propose an approach that allows AVs to learn the decision-making of
HVs implicitly from experience, account for all vehicles' interests, and safely
adapt to other traffic situations. In contrast with existing works, we quantify
AVs' social preferences and propose a distributed reward structure that
introduces altruism into their decision-making process, allowing the altruistic
AVs to learn to establish coalitions and influence the behavior of HVs.Comment: arXiv admin note: substantial text overlap with arXiv:2202.0088
Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving
Autonomous vehicle (AV) navigation in the presence of Human-driven vehicles
(HVs) is challenging, as HVs continuously update their policies in response to
AVs. In order to navigate safely in the presence of complex AV-HV social
interactions, the AVs must learn to predict these changes. Humans are capable
of navigating such challenging social interaction settings because of their
intrinsic knowledge about other agents behaviors and use that to forecast what
might happen in the future. Inspired by humans, we provide our AVs the
capability of anticipating future states and leveraging prediction in a
cooperative reinforcement learning (RL) decision-making framework, to improve
safety and robustness. In this paper, we propose an integration of two
essential and earlier-presented components of AVs: social navigation and
prediction. We formulate the AV decision-making process as a RL problem and
seek to obtain optimal policies that produce socially beneficial results
utilizing a prediction-aware planning and social-aware optimization RL
framework. We also propose a Hybrid Predictive Network (HPN) that anticipates
future observations. The HPN is used in a multi-step prediction chain to
compute a window of predicted future observations to be used by the value
function network (VFN). Finally, a safe VFN is trained to optimize a social
utility using a sequence of previous and predicted observations, and a safety
prioritizer is used to leverage the interpretable kinematic predictions to mask
the unsafe actions, constraining the RL policy. We compare our prediction-aware
AV to state-of-the-art solutions and demonstrate performance improvements in
terms of efficiency and safety in multiple simulated scenarios
Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems
Vehicle-to-Everything (V2X) communication has been proposed as a potential
solution to improve the robustness and safety of autonomous vehicles by
improving coordination and removing the barrier of non-line-of-sight sensing.
Cooperative Vehicle Safety (CVS) applications are tightly dependent on the
reliability of the underneath data system, which can suffer from loss of
information due to the inherent issues of their different components, such as
sensors failures or the poor performance of V2X technologies under dense
communication channel load. Particularly, information loss affects the target
classification module and, subsequently, the safety application performance. To
enable reliable and robust CVS systems that mitigate the effect of information
loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled
with a hybrid learning-based predictive modeling technique for CVS systems. The
CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian
Process (HGP) prediction system. Consequently, the vehicle safety applications
use the information from the CA-TC, making them more robust and reliable. The
CAM leverages vehicles path history, road geometry, tracking, and prediction;
and the HGP is utilized to provide accurate vehicles' trajectory predictions to
compensate for data loss (due to communication congestion) or sensor
measurements' inaccuracies. Based on offline real-world data, we learn a finite
bank of driver models that represent the joint dynamics of the vehicle and the
drivers' behavior. We combine offline training and online model updates with
on-the-fly forecasting to account for new possible driver behaviors. Finally,
our framework is validated using simulation and realistic driving scenarios to
confirm its potential in enhancing the robustness and reliability of CVS
systems
Inadecuado uso de residuos s贸lidos y su impacto en la contaminaci贸n ambiental
La presente investigaci贸n se desarroll贸 con el prop贸sito de determinar el impacto del inadecuado uso de residuos s贸lidos en la contaminaci贸n ambiental del distrito de Julcan, Per煤. Se trabaj贸 con una muestra de 70 viviendas; as铆 mismo se ha empleado dos cuestionarios confiables y debidamente validados para la recolecci贸n de datos de las variables en estudio y se proces贸 la informaci贸n a trav茅s del software de estad铆stica para ciencias sociales (SPSS V23). Se concluy贸 que el inadecuado uso de residuos s贸lidos impacta en la contaminaci贸n ambiental seg煤n el coeficiente de contingencia del estad铆stico de prueba Tau-b de Kendall es -0,180, con un nivel de significancia menor al 5% de significancia est谩ndar (P= 0,042 < 0,05), asimismo observamos que el estad铆stico Rho de Spearman es -0,252, con un nivel de significancia menor al 5% de significancia est谩ndar (P= 0,045 < 0,05)