2,682 research outputs found
Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning
The majority of current studies on autonomous vehicle control via deep
reinforcement learning (DRL) utilize point-mass kinematic models, neglecting
vehicle dynamics which includes acceleration delay and acceleration command
dynamics. The acceleration delay, which results from sensing and actuation
delays, results in delayed execution of the control inputs. The acceleration
command dynamics dictates that the actual vehicle acceleration does not rise up
to the desired command acceleration instantaneously due to dynamics. In this
work, we investigate the feasibility of applying DRL controllers trained using
vehicle kinematic models to more realistic driving control with vehicle
dynamics. We consider a particular longitudinal car-following control, i.e.,
Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass
kinematic model. When such a controller is applied to car following with
vehicle dynamics, we observe significantly degraded car-following performance.
Therefore, we redesign the DRL framework to accommodate the acceleration delay
and acceleration command dynamics by adding the delayed control inputs and the
actual vehicle acceleration to the reinforcement learning environment state,
respectively. The training results show that the redesigned DRL controller
results in near-optimal control performance of car following with vehicle
dynamics considered when compared with dynamic programming solutions.Comment: Accepted to 2019 IEEE Intelligent Transportation Systems Conferenc
Predictive World Models from Real-World Partial Observations
Cognitive scientists believe adaptable intelligent agents like humans perform
reasoning through learned causal mental simulations of agents and environments.
The problem of learning such simulations is called predictive world modeling.
Recently, reinforcement learning (RL) agents leveraging world models have
achieved SOTA performance in game environments. However, understanding how to
apply the world modeling approach in complex real-world environments relevant
to mobile robots remains an open question. In this paper, we present a
framework for learning a probabilistic predictive world model for real-world
road environments. We implement the model using a hierarchical VAE (HVAE)
capable of predicting a diverse set of fully observed plausible worlds from
accumulated sensor observations. While prior HVAE methods require complete
states as ground truth for learning, we present a novel sequential training
method to allow HVAEs to learn to predict complete states from partially
observed states only. We experimentally demonstrate accurate spatial structure
prediction of deterministic regions achieving 96.21 IoU, and close the gap to
perfect prediction by 62% for stochastic regions using the best prediction. By
extending HVAEs to cases where complete ground truth states do not exist, we
facilitate continual learning of spatial prediction as a step towards realizing
explainable and comprehensive predictive world models for real-world mobile
robotics applications. Code is available at
https://github.com/robin-karlsson0/predictive-world-models.Comment: Accepted for IEEE MOST 202
End-to-end Autonomous Driving: Challenges and Frontiers
The autonomous driving community has witnessed a rapid growth in approaches
that embrace an end-to-end algorithm framework, utilizing raw sensor input to
generate vehicle motion plans, instead of concentrating on individual tasks
such as detection and motion prediction. End-to-end systems, in comparison to
modular pipelines, benefit from joint feature optimization for perception and
planning. This field has flourished due to the availability of large-scale
datasets, closed-loop evaluation, and the increasing need for autonomous
driving algorithms to perform effectively in challenging scenarios. In this
survey, we provide a comprehensive analysis of more than 250 papers, covering
the motivation, roadmap, methodology, challenges, and future trends in
end-to-end autonomous driving. We delve into several critical challenges,
including multi-modality, interpretability, causal confusion, robustness, and
world models, amongst others. Additionally, we discuss current advancements in
foundation models and visual pre-training, as well as how to incorporate these
techniques within the end-to-end driving framework. To facilitate future
research, we maintain an active repository that contains up-to-date links to
relevant literature and open-source projects at
https://github.com/OpenDriveLab/End-to-end-Autonomous-Driving
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