860 research outputs found
A Probabilistic Framework for Imitating Human Race Driver Behavior
Understanding and modeling human driver behavior is crucial for advanced
vehicle development. However, unique driving styles, inconsistent behavior, and
complex decision processes render it a challenging task, and existing
approaches often lack variability or robustness. To approach this problem, we
propose Probabilistic Modeling of Driver behavior (ProMoD), a modular framework
which splits the task of driver behavior modeling into multiple modules. A
global target trajectory distribution is learned with Probabilistic Movement
Primitives, clothoids are utilized for local path generation, and the
corresponding choice of actions is performed by a neural network. Experiments
in a simulated car racing setting show considerable advantages in imitation
accuracy and robustness compared to other imitation learning algorithms. The
modular architecture of the proposed framework facilitates straightforward
extensibility in driving line adaptation and sequencing of multiple movement
primitives for future research.Comment: updated references [17] and [33]; added journal inf
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
Learning Multi-Modal Self-Awareness Models Empowered by Active Inference for Autonomous Vehicles
For autonomous agents to coexist with the real world, it is essential to anticipate the dynamics and interactions in their surroundings. Autonomous agents can use models of the human
brain to learn about responding to the actions of other participants in the environment and proactively coordinates with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant intents. A neglected problem has long been understanding and processing environmental
perception data from the multisensorial information referring to the cognitive psychology level of the human brain process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is
supposed to possess the mechanism of memorizing, inferring, and experiential updating, enabling it to cope with the changes in an external world. Therefore, a practical self-driving approach should be open to more than just the traditional computing structure of perception, planning, decision-making, and control. It is necessary to explore a probabilistic
framework that goes along with human brain attention, reasoning, learning, and decisionmaking mechanism concerning interactive behavior and build an intelligent system inspired by biological intelligence.
This thesis presents a multi-modal self-awareness module for autonomous driving systems. The techniques proposed in this research are evaluated on their ability to model proper driving behavior in dynamic environments, which is vital in autonomous driving for both action
planning and safe navigation. First, this thesis adapts generative incremental learning to the problem of imitation learning. It extends the imitation learning framework to work in the multi-agent setting where observations gathered from multiple agents are used to
inform the training process of a learning agent, which tracks a dynamic target. Since driving has associated rules, the second part of this thesis introduces a method to provide optimal knowledge to the imitation learning agent through an active inference approach.
Active inference is the selective information method gathering during prediction to increase a predictive machine learning model’s prediction performance. Finally, to address the inference complexity and solve the exploration-exploitation dilemma in unobserved environments, an exploring action-oriented model is introduced by pulling together imitation learning and active inference methods inspired by the brain learning procedure
Learning Multi-Modal Self-Awareness Models Empowered by Active Inference for Autonomous Vehicles
MenciĂłn Internacional en el tĂtulo de doctorFor autonomous agents to coexist with the real world, it is essential to anticipate the dynamics
and interactions in their surroundings. Autonomous agents can use models of the human
brain to learn about responding to the actions of other participants in the environment
and proactively coordinates with the dynamics. Modeling brain learning procedures is
challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant
intents. A neglected problem has long been understanding and processing environmental
perception data from the multisensorial information referring to the cognitive psychology
level of the human brain process. The key to solving this problem is to construct a computing
model with selective attention and self-learning ability for autonomous driving, which is
supposed to possess the mechanism of memorizing, inferring, and experiential updating,
enabling it to cope with the changes in an external world. Therefore, a practical selfdriving
approach should be open to more than just the traditional computing structure of
perception, planning, decision-making, and control. It is necessary to explore a probabilistic
framework that goes along with human brain attention, reasoning, learning, and decisionmaking
mechanism concerning interactive behavior and build an intelligent system inspired
by biological intelligence.
This thesis presents a multi-modal self-awareness module for autonomous driving systems.
The techniques proposed in this research are evaluated on their ability to model proper driving
behavior in dynamic environments, which is vital in autonomous driving for both action
planning and safe navigation. First, this thesis adapts generative incremental learning to
the problem of imitation learning. It extends the imitation learning framework to work
in the multi-agent setting where observations gathered from multiple agents are used to
inform the training process of a learning agent, which tracks a dynamic target. Since
driving has associated rules, the second part of this thesis introduces a method to provide
optimal knowledge to the imitation learning agent through an active inference approach.
Active inference is the selective information method gathering during prediction to increase a
predictive machine learning model’s prediction performance. Finally, to address the inference
complexity and solve the exploration-exploitation dilemma in unobserved environments, an exploring action-oriented model is introduced by pulling together imitation learning and
active inference methods inspired by the brain learning procedure.Programa de Doctorado en IngenierĂa ElĂ©ctrica, ElectrĂłnica y Automática por la Universidad Carlos III de MadridPresidente: Marco Carli.- Secretario: VĂctor González Castro.- Vocal: Nicola Conc
Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches
The growing advancements in Autonomous Vehicles (AVs) have emphasized the
critical need to prioritize the absolute safety of AV maneuvers, especially in
dynamic and unpredictable environments or situations. This objective becomes
even more challenging due to the uniqueness of every traffic
situation/condition. To cope with all these very constrained and complex
configurations, AVs must have appropriate control architectures with reliable
and real-time Risk Assessment and Management Strategies (RAMS). These targeted
RAMS must lead to reduce drastically the navigation risks. However, the lack of
safety guarantees proves, which is one of the key challenges to be addressed,
limit drastically the ambition to introduce more broadly AVs on our roads and
restrict the use of AVs to very limited use cases. Therefore, the focus and the
ambition of this paper is to survey research on autonomous vehicles while
focusing on the important topic of safety guarantee of AVs. For this purpose,
it is proposed to review research on relevant methods and concepts defining an
overall control architecture for AVs, with an emphasis on the safety assessment
and decision-making systems composing these architectures. Moreover, it is
intended through this reviewing process to highlight researches that use either
model-based methods or AI-based approaches. This is performed while emphasizing
the strengths and weaknesses of each methodology and investigating the research
that proposes a comprehensive multi-modal design that combines model-based and
AI approaches. This paper ends with discussions on the methods used to
guarantee the safety of AVs namely: safety verification techniques and the
standardization/generalization of safety frameworks
This is the Way: Differential Bayesian Filtering for Agile Trajectory Synthesis
One of the main challenges in autonomous racing is to design algorithms for
motion planning at high speed, and across complex racing courses. End-to-end
trajectory synthesis has been previously proposed where the trajectory for the
ego vehicle is computed based on camera images from the racecar. This is done
in a supervised learning setting using behavioral cloning techniques. In this
paper, we address the limitations of behavioral cloning methods for trajectory
synthesis by introducing Differential Bayesian Filtering (DBF), which uses
probabilistic B\'ezier curves as a basis for inferring optimal autonomous
racing trajectories based on Bayesian inference. We introduce a trajectory
sampling mechanism and combine it with a filtering process which is able to
push the car to its physical driving limits. The performance of DBF is
evaluated on the DeepRacing Formula One simulation environment and compared
with several other trajectory synthesis approaches as well as human driving
performance. DBF achieves the fastest lap time, and the fastest speed, by
pushing the racecar closer to its limits of control while always remaining
inside track bounds.Comment: 8 page
Safe-by-Construction Autonomous Vehicle Overtaking using Control Barrier Functions and Model Predictive Control
Ensuring safety for vehicle overtaking systems is one of the most fundamental
and challenging tasks in autonomous driving. This task is particularly
intricate when the vehicle must not only overtake its front vehicle safely but
also consider the presence of potential opposing vehicles in the opposite lane
that it will temporarily occupy. In order to tackle the overtaking task in such
challenging scenarios, we introduce a novel integrated framework tailored for
vehicle overtaking maneuvers. Our approach integrates the theories of
varying-level control barrier functions (CBF) and time-optimal model predictive
control (MPC). The main feature of our proposed overtaking strategy is that it
is safe-by-construction, which enables rigorous mathematical proof and
validation of the safety guarantees. We show that the proposed framework is
applicable when the opposing vehicle is either fully autonomous or driven by
human drivers. To demonstrate our framework, we perform a set of simulations
for overtaking scenarios under different settings. The simulation results show
the superiority of our framework in the sense that it ensures collision-free
and achieves better safety performance compared with the standard MPC-based
approach without safety guarantees
- …