39,972 research outputs found
A Time Efficient Approach for Decision-Making Style Recognition in Lane-Change Behavior
Fast recognizing driver's decision-making style of changing lanes plays a
pivotal role in safety-oriented and personalized vehicle control system design.
This paper presents a time-efficient recognition method by integrating k-means
clustering (k-MC) with K-nearest neighbor (KNN), called kMC-KNN. The
mathematical morphology is implemented to automatically label the
decision-making data into three styles (moderate, vague, and aggressive), while
the integration of kMC and KNN helps to improve the recognition speed and
accuracy. Our developed mathematical morphology-based clustering algorithm is
then validated by comparing to agglomerative hierarchical clustering.
Experimental results demonstrate that the developed kMC-KNN method, in
comparison to the traditional KNN, can shorten the recognition time by over
72.67% with recognition accuracy of 90%-98%. In addition, our developed kMC-KNN
method also outperforms the support vector machine (SVM) in recognition
accuracy and stability. The developed time-efficient recognition approach would
have great application potential to the in-vehicle embedded solutions with
restricted design specifications
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
Situational awareness in vehicular networks could be substantially improved
utilizing reliable trajectory prediction methods. More precise situational
awareness, in turn, results in notably better performance of critical safety
applications, such as Forward Collision Warning (FCW), as well as comfort
applications like Cooperative Adaptive Cruise Control (CACC). Therefore,
vehicle trajectory prediction problem needs to be deeply investigated in order
to come up with an end to end framework with enough precision required by the
safety applications' controllers. This problem has been tackled in the
literature using different methods. However, machine learning, which is a
promising and emerging field with remarkable potential for time series
prediction, has not been explored enough for this purpose. In this paper, a
two-layer neural network-based system is developed which predicts the future
values of vehicle parameters, such as velocity, acceleration, and yaw rate, in
the first layer and then predicts the two-dimensional, i.e. longitudinal and
lateral, trajectory points based on the first layer's outputs. The performance
of the proposed framework has been evaluated in realistic cut-in scenarios from
Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable
improvement in the prediction accuracy in comparison with the kinematics model
which is the dominant employed model by the automotive industry. Both ideal and
nonideal communication circumstances have been investigated for our system
evaluation. For non-ideal case, an estimation step is included in the framework
before the parameter prediction block to handle the drawbacks of packet drops
or sensor failures and reconstruct the time series of vehicle parameters at a
desirable frequency
Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
Interpretation of common-yet-challenging interaction scenarios can benefit
well-founded decisions for autonomous vehicles. Previous research achieved this
using their prior knowledge of specific scenarios with predefined models,
limiting their adaptive capabilities. This paper describes a Bayesian
nonparametric approach that leverages continuous (i.e., Gaussian processes) and
discrete (i.e., Dirichlet processes) stochastic processes to reveal underlying
interaction patterns of the ego vehicle with other nearby vehicles. Our model
relaxes dependency on the number of surrounding vehicles by developing an
acceleration-sensitive velocity field based on Gaussian processes. The
experiment results demonstrate that the velocity field can represent the
spatial interactions between the ego vehicle and its surroundings. Then, a
discrete Bayesian nonparametric model, integrating Dirichlet processes and
hidden Markov models, is developed to learn the interaction patterns over the
temporal space by segmenting and clustering the sequential interaction data
into interpretable granular patterns automatically. We then evaluate our
approach in the highway lane-change scenarios using the highD dataset collected
from real-world settings. Results demonstrate that our proposed Bayesian
nonparametric approach provides an insight into the complicated lane-change
interactions of the ego vehicle with multiple surrounding traffic participants
based on the interpretable interaction patterns and their transition properties
in temporal relationships. Our proposed approach sheds light on efficiently
analyzing other kinds of multi-agent interactions, such as vehicle-pedestrian
interactions. View the demos via https://youtu.be/z_vf9UHtdAM.Comment: for the supplements, see
https://chengyuan-zhang.github.io/Multivehicle-Interaction
Imitating Driver Behavior with Generative Adversarial Networks
The ability to accurately predict and simulate human driving behavior is
critical for the development of intelligent transportation systems. Traditional
modeling methods have employed simple parametric models and behavioral cloning.
This paper adopts a method for overcoming the problem of cascading errors
inherent in prior approaches, resulting in realistic behavior that is robust to
trajectory perturbations. We extend Generative Adversarial Imitation Learning
to the training of recurrent policies, and we demonstrate that our model
outperforms rule-based controllers and maximum likelihood models in realistic
highway simulations. Our model both reproduces emergent behavior of human
drivers, such as lane change rate, while maintaining realistic control over
long time horizons.Comment: 8 pages, 6 figure
Applicability of Neural Networks for Driving Style Classification and Maneuver Detection
Maneuver and driving style detection are of ongoing interest for the extension of vehicle's functionalities. Existing machine learning approaches require extensive sensor data and demand for high computational power. For vehicle onboard implementation, poorly generalizing rule-based approaches are currently state of the art. Not being restricted to neither comprehensive environmental sensors like camera or radar, nor high computing power (both of what is today only present in upper class' vehicles), our approach allows for cross-vehicle use: In this work, the applicability of small artificial neural networks (ANN) as efficient detectors is tested using a prototypal vehicle implementation. During test drives, overtaking maneuvers have been detected 1.2 s prior to the competing rule-based approach in average, also greatly improving the detection performance. Regarding driving style recognition, ANN-based results are closer to targets and more patient at driving style transitions. A recognition rate of over 75 % is achieved
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