197 research outputs found
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes
This paper presents two variations of a novel stochastic prediction algorithm
that enables mobile robots to accurately and robustly predict the future state
of complex dynamic scenes. The proposed algorithm uses a variational
autoencoder to predict a range of possible future states of the environment.
The algorithm takes full advantage of the motion of the robot itself, the
motion of dynamic objects, and the geometry of static objects in the scene to
improve prediction accuracy. Three simulated and real-world datasets collected
by different robot models are used to demonstrate that the proposed algorithm
is able to achieve more accurate and robust prediction performance than other
prediction algorithms. Furthermore, a predictive uncertainty-aware planner is
proposed to demonstrate the effectiveness of the proposed predictor in
simulation and real-world navigation experiments. Implementations are open
source at https://github.com/TempleRAIL/SOGMP.Comment: Accepted by 7th Annual Conference on Robot Learning (CoRL), 202
Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review
Behaviour prediction function of an autonomous vehicle predicts the future
states of the nearby vehicles based on the current and past observations of the
surrounding environment. This helps enhance their awareness of the imminent
hazards. However, conventional behaviour prediction solutions are applicable in
simple driving scenarios that require short prediction horizons. Most recently,
deep learning-based approaches have become popular due to their superior
performance in more complex environments compared to the conventional
approaches. Motivated by this increased popularity, we provide a comprehensive
review of the state-of-the-art of deep learning-based approaches for vehicle
behaviour prediction in this paper. We firstly give an overview of the generic
problem of vehicle behaviour prediction and discuss its challenges, followed by
classification and review of the most recent deep learning-based solutions
based on three criteria: input representation, output type, and prediction
method. The paper also discusses the performance of several well-known
solutions, identifies the research gaps in the literature and outlines
potential new research directions
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