1,617 research outputs found
Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets
By observing their environment as well as other traffic participants, humans
are enabled to drive road vehicles safely. Vehicle passengers, however,
perceive a notable difference between non-experienced and experienced drivers.
In particular, they may get the impression that the latter ones anticipate what
will happen in the next few moments and consider these foresights in their
driving behavior. To make the driving style of automated vehicles comparable to
the one of human drivers with respect to comfort and perceived safety, the
aforementioned anticipation skills need to become a built-in feature of
self-driving vehicles. This article provides a systematic comparison of methods
and strategies to generate this intention for self-driving cars using machine
learning techniques. To implement and test these algorithms we use a large data
set collected over more than 30000 km of highway driving and containing
approximately 40000 real-world driving situations. We further show that it is
possible to classify driving maneuvers upcoming within the next 5 s with an
Area Under the ROC Curve (AUC) above 0.92 for all defined maneuver classes.
This enables us to predict the lateral position with a prediction horizon of 5
s with a median lateral error of less than 0.21 m.Comment: the paper has been accepted for publication in IEEE Transcations on
Intelligent Transportation Systems (T-ITS) 16 pages 13 figures 12 table
Lane Change Classification and Prediction with Action Recognition Networks
Anticipating lane change intentions of surrounding vehicles is crucial for
efficient and safe driving decision making in an autonomous driving system.
Previous works often adopt physical variables such as driving speed,
acceleration and so forth for lane change classification. However, physical
variables do not contain semantic information. Although 3D CNNs have been
developing rapidly, the number of methods utilising action recognition models
and appearance feature for lane change recognition is low, and they all require
additional information to pre-process data. In this work, we propose an
end-to-end framework including two action recognition methods for lane change
recognition, using video data collected by cameras. Our method achieves the
best lane change classification results using only the RGB video data of the
PREVENTION dataset. Class activation maps demonstrate that action recognition
models can efficiently extract lane change motions. A method to better extract
motion clues is also proposed in this paper.Comment: Accepted by ECC
Recommended from our members
Fast, Scalable, and Accurate Algorithms for Time-Series Analysis
Time is a critical element for the understanding of natural processes (e.g., earthquakes and weather) or human-made artifacts (e.g., stock market and speech signals). The analysis of time series, the result of sequentially collecting observations of such processes and artifacts, is becoming increasingly prevalent across scientific and industrial applications. The extraction of non-trivial features (e.g., patterns, correlations, and trends) in time series is a critical step for devising effective time-series mining methods for real-world problems and the subject of active research for decades. In this dissertation, we address this fundamental problem by studying and presenting computational methods for efficient unsupervised learning of robust feature representations from time series. Our objective is to (i) simplify and unify the design of scalable and accurate time-series mining algorithms; and (ii) provide a set of readily available tools for effective time-series analysis. We focus on applications operating solely over time-series collections and on applications where the analysis of time series complements the analysis of other types of data, such as text and graphs.
For applications operating solely over time-series collections, we propose a generic computational framework, GRAIL, to learn low-dimensional representations that natively preserve the invariances offered by a given time-series comparison method. GRAIL represents a departure from classic approaches in the time-series literature where representation methods are agnostic to the similarity function used in subsequent learning processes. GRAIL relies on the attractive idea that once we construct the data-to-data similarity matrix most time-series mining tasks can be trivially solved. To overcome scalability issues associated with approaches relying on such matrices, GRAIL exploits time-series clustering to construct a small set of landmark time series and learns representations to reduce the data-to-data matrix to a data-to-landmark points matrix. To demonstrate the effectiveness of GRAIL, we first present domain-independent, highly accurate, and scalable time-series clustering methods to facilitate exploration and summarization of time-series collections. Then, we show that GRAIL representations, when combined with suitable methods, significantly outperform, in terms of efficiency and accuracy, state-of-the-art methods in major time-series mining tasks, such as querying, clustering, classification, sampling, and visualization. Overall, GRAIL rises as a new primitive for highly accurate, yet scalable, time-series analysis.
For applications where the analysis of time series complements the analysis of other types of data, such as text and graphs, we propose generic, simple, and lightweight methodologies to learn features from time-varying measurements. Such applications often organize operations over different types of data in a pipeline such that one operation provides input---in the form of feature vectors---to subsequent operations. To reason about the temporal patterns and trends in the underlying features, we need to (i) track the evolution of features over different time periods; and (ii) transform these time-varying features into actionable knowledge (e.g., forecasting an outcome). To address this challenging problem, we propose principled approaches to model time-varying features and study two large-scale, real-world, applications. Specifically, we first study the problem of predicting the impact of scientific concepts through temporal analysis of characteristics extracted from the metadata and full text of scientific articles. Then, we explore the promise of harnessing temporal patterns in behavioral signals extracted from web search engine logs for early detection of devastating diseases. In both applications, combinations of features with time-series relevant features yielded the greatest impact than any other indicator considered in our analysis. We believe that our simple methodology, along with the interesting domain-specific findings that our work revealed, will motivate new studies across different scientific and industrial settings
- …