16,384 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles
Interaction-aware Autonomous Driving (IAAD) is a rapidly growing field of
research that focuses on the development of autonomous vehicles (AVs) that are
capable of interacting safely and efficiently with human road users. This is a
challenging task, as it requires the autonomous vehicle to be able to
understand and predict the behaviour of human road users. In this literature
review, the current state of IAAD research is surveyed in this work. Commencing
with an examination of terminology, attention is drawn to challenges and
existing models employed for modelling the behaviour of drivers and
pedestrians. Next, a comprehensive review is conducted on various techniques
proposed for interaction modelling, encompassing cognitive methods, machine
learning approaches, and game-theoretic methods. The conclusion is reached
through a discussion of potential advantages and risks associated with IAAD,
along with the illumination of pivotal research inquiries necessitating future
exploration
Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving
Decision-making for urban autonomous driving is challenging due to the
stochastic nature of interactive traffic participants and the complexity of
road structures. Although reinforcement learning (RL)-based decision-making
scheme is promising to handle urban driving scenarios, it suffers from low
sample efficiency and poor adaptability. In this paper, we propose Scene-Rep
Transformer to improve the RL decision-making capabilities with better scene
representation encoding and sequential predictive latent distillation.
Specifically, a multi-stage Transformer (MST) encoder is constructed to model
not only the interaction awareness between the ego vehicle and its neighbors
but also intention awareness between the agents and their candidate routes. A
sequential latent Transformer (SLT) with self-supervised learning objectives is
employed to distill the future predictive information into the latent scene
representation, in order to reduce the exploration space and speed up training.
The final decision-making module based on soft actor-critic (SAC) takes as
input the refined latent scene representation from the Scene-Rep Transformer
and outputs driving actions. The framework is validated in five challenging
simulated urban scenarios with dense traffic, and its performance is manifested
quantitatively by the substantial improvements in data efficiency and
performance in terms of success rate, safety, and efficiency. The qualitative
results reveal that our framework is able to extract the intentions of neighbor
agents to help make decisions and deliver more diversified driving behaviors
- …