38 research outputs found
Optimization of shift schedule for hybrid electric vehicle with automated manual transmission
Currently, most hybrid electric vehicles (HEVs) equipped with automated mechanical transmission (AMT) are implemented with the conventional two-parameter gear shift schedule based on engineering experience. However, this approach cannot take full advantage of hybrid drives. In other words, the powertrain of an HEV is not able to work at the best fuel-economy points during the whole driving profile. To solve this problem, an optimization method of gear shift schedule for HEVs is proposed based on Dynamic Programming (DP) and a corresponding solving algorithm is also put forward. A gear shift schedule that can be employed in real-vehicle is extracted from the obtained optimal gear shift points by DP approach and is optimized based on analysis of the engineering experience in a typical Chinese urban driving cycle. Compared with the conventional two-parameter gear shift schedule in both simulation and real vehicle experiments, the extracted gear shift schedule is proved to clearly improve the fuel economy of the HEV
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
Statistical‐based approach for driving style recognition using Bayesian probability with kernel density estimation
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166283/1/itr2bf00581.pd
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
Research on Shifting Control Method of Positive Independent Mechanical Split Path Transmission for the Starting Gear
To realize a smooth and quick shift of the positive independent mechanical split path transmission (PIMSPT) equipped with automatic shifting control system (ASCS), the research on the feasibility of improving shift quality by dynamic and cooperative controlling engine, steering clutches, and brakes has been conducted. The shifting control method suited to starting gear of PIMSPT has been proposed. The control method is based on control parameters, such as the driving shaft speed and its derivative. The control laws of steering clutches and brakes are presented during each gear and stage of shifting. Bench and road test results show that the proposed shifting control method can not only shorten the shift time, but also decrease the jerk of shifting effectively