44 research outputs found
Increment entropy as a measure of complexity for time series
Entropy has been a common index to quantify the complexity of time series in
a variety of fields. Here, we introduce increment entropy to measure the
complexity of time series in which each increment is mapped into a word of two
letters, one letter corresponding to direction and the other corresponding to
magnitude. The Shannon entropy of the words is termed as increment entropy
(IncrEn). Simulations on synthetic data and tests on epileptic EEG signals have
demonstrated its ability of detecting the abrupt change, regardless of
energetic (e.g. spikes or bursts) or structural changes. The computation of
IncrEn does not make any assumption on time series and it can be applicable to
arbitrary real-world data.Comment: 12pages,7figure,2 table
Sparse Semantic Map-Based Monocular Localization in Traffic Scenes Using Learned 2D-3D Point-Line Correspondences
Vision-based localization in a prior map is of crucial importance for
autonomous vehicles. Given a query image, the goal is to estimate the camera
pose corresponding to the prior map, and the key is the registration problem of
camera images within the map. While autonomous vehicles drive on the road under
occlusion (e.g., car, bus, truck) and changing environment appearance (e.g.,
illumination changes, seasonal variation), existing approaches rely heavily on
dense point descriptors at the feature level to solve the registration problem,
entangling features with appearance and occlusion. As a result, they often fail
to estimate the correct poses. To address these issues, we propose a sparse
semantic map-based monocular localization method, which solves 2D-3D
registration via a well-designed deep neural network. Given a sparse semantic
map that consists of simplified elements (e.g., pole lines, traffic sign
midpoints) with multiple semantic labels, the camera pose is then estimated by
learning the corresponding features between the 2D semantic elements from the
image and the 3D elements from the sparse semantic map. The proposed sparse
semantic map-based localization approach is robust against occlusion and
long-term appearance changes in the environments. Extensive experimental
results show that the proposed method outperforms the state-of-the-art
approaches
FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction
Predicting the future trajectories of the traffic agents is a gordian
technique in autonomous driving. However, trajectory prediction suffers from
data imbalance in the prevalent datasets, and the tailed data is often more
complicated and safety-critical. In this paper, we focus on dealing with the
long-tail phenomenon in trajectory prediction. Previous methods dealing with
long-tail data did not take into account the variety of motion patterns in the
tailed data. In this paper, we put forward a future enhanced contrastive
learning framework to recognize tail trajectory patterns and form a feature
space with separate pattern clusters. Furthermore, a distribution aware hyper
predictor is brought up to better utilize the shaped feature space. Our method
is a model-agnostic framework and can be plugged into many well-known
baselines. Experimental results show that our framework outperforms the
state-of-the-art long-tail prediction method on tailed samples by 9.5% on ADE
and 8.5% on FDE, while maintaining or slightly improving the averaged
performance. Our method also surpasses many long-tail techniques on trajectory
prediction task.Comment: Accepted for publication at the IEEE/CVF Conference on Computer
Vision and Pattern Recognition 2023 (CVPR 2023
Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning
Heterogeneous trajectory forecasting is critical for intelligent
transportation systems, while it is challenging because of the difficulty for
modeling the complex interaction relations among the heterogeneous road agents
as well as their agent-environment constraint. In this work, we propose a risk
and scene graph learning method for trajectory forecasting of heterogeneous
road agents, which consists of a Heterogeneous Risk Graph (HRG) and a
Hierarchical Scene Graph (HSG) from the aspects of agent category and their
movable semantic regions. HRG groups each kind of road agents and calculates
their interaction adjacency matrix based on an effective collision risk metric.
HSG of driving scene is modeled by inferring the relationship between road
agents and road semantic layout aligned by the road scene grammar. Based on
this formulation, we can obtain an effective trajectory forecasting in driving
situations, and superior performance to other state-of-the-art approaches is
demonstrated by exhaustive experiments on the nuScenes, ApolloScape, and
Argoverse datasets.Comment: Submitted to IEEE Transactions on Intelligent Transportation Systems,
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