667 research outputs found
A Deep Learning Based Model for Driving Risk Assessment
In this paper a novel multilayer model is proposed for assessing driving risk. Studying aggressive behavior via massive driving data is essential for protecting road traffic safety and reducing losses of human life and property in smart city context. In particular, identifying aggressive behavior and driving risk are multi-factors combined evaluation process, which must be processed with time and environment. For instance, improper time and environment may facilitate abnormal driving behavior. The proposed Dynamic Multilayer Model consists of identifying instant aggressive driving behavior that can be visited within specific time windows and calculating individual driving risk via Deep Neural Networks based classification algorithms. Validation results show that the proposed methods are particularly effective for identifying driving aggressiveness and risk level via real dataset of 2129 driversâ driving behavior
Shareable Driving Style Learning and Analysis with a Hierarchical Latent Model
Driving style is usually used to characterize driving behavior for a driver
or a group of drivers. However, it remains unclear how one individual's driving
style shares certain common grounds with other drivers. Our insight is that
driving behavior is a sequence of responses to the weighted mixture of latent
driving styles that are shareable within and between individuals. To this end,
this paper develops a hierarchical latent model to learn the relationship
between driving behavior and driving styles. We first propose a fragment-based
approach to represent complex sequential driving behavior, allowing for
sufficiently representing driving behavior in a low-dimension feature space.
Then, we provide an analytical formulation for the interaction of driving
behavior and shareable driving style with a hierarchical latent model by
introducing the mechanism of Dirichlet allocation. Our developed model is
finally validated and verified with 100 drivers in naturalistic driving
settings with urban and highways. Experimental results reveal that individuals
share driving styles within and between them. We also analyzed the influence of
personalities (e.g., age, gender, and driving experience) on driving styles and
found that a naturally aggressive driver would not always keep driving
aggressively (i.e., could behave calmly sometimes) but with a higher proportion
of aggressiveness than other types of drivers
Implicit personalization in driving assistance: State-of-the-art and open issues
In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2
Personalized driver workload inference by learning from vehicle related measurements
Adapting in-vehicle systems (e.g. Advanced Driver Assistance Systems, In-Vehicle Information Systems) to individual driversâ workload can enhance safety and convenience. To make this possible, it is a prerequisite to infer driver workload so that adaptive aiding can be provided to the driver at the right time and in a proper manner. Rather than developing an average model for all drivers, a Personalized Driver Workload Inference (PDWI)
system considering individual driversâ driving characteristics is developed using machine learning techniques via easily accessed
Vehicle Related Measurements (VRMs). The proposed PDWI system comprises two stages. In offline training, individual driversâ workload is first automatically splitted into different categories according to its inherent data characteristics using Fuzzy C means clustering. Then an implicit mapping between VRMs
and different levels of workload is constructed via classification algorithms. In online implementation, VRMs samples are classified
into different clusters, consequently driver workload can be successfully inferred. A recently collected dataset from real-world
naturalistic driving experiments is drawn to validate the proposed PDWI system. Comparative experimental results indicate that the proposed framework integrating Fuzzy C-means clustering and Support Vector Machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, F1-score and prediction time. The inter-individual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness
FedDriveScore: Federated Scoring Driving Behavior with a Mixture of Metric Distributions
Scoring the driving performance of various drivers on a unified scale, based
on how safe or economical they drive on their daily trips, is essential for the
driver profile task. Connected vehicles provide the opportunity to collect
real-world driving data, which is advantageous for constructing scoring models.
However, the lack of pre-labeled scores impede the use of supervised regression
models and the data privacy issues hinder the way of traditionally
data-centralized learning on the cloud side for model training. To address
them, an unsupervised scoring method is presented without the need for labels
while still preserving fairness and objectiveness compared to subjective
scoring strategies. Subsequently, a federated learning framework based on
vehicle-cloud collaboration is proposed as a privacy-friendly alternative to
centralized learning. This framework includes a consistently federated version
of the scoring method to reduce the performance degradation of the global
scoring model caused by the statistical heterogeneous challenge of local data.
Theoretical and experimental analysis demonstrate that our federated scoring
model is consistent with the utility of the centrally learned counterpart and
is effective in evaluating driving performance
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