Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron

Shou, Zhenyu; Wang, Ziran; Han, Kyungtae; Liu, Yongkang; Tiwari, Prashant; Di, Xuan

doi:10.1109/iv47402.2020.9304587

Cited by 34 publications

(13 citation statements)

References 20 publications

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“…A three-layer multilayer perceptron (MLP) classifier is utilized in our lane change prediction model [30]. Features we explored include speed of the ego-vehicle, speed difference between the ego-vehicle and its neighboring vehicles, and longitudinal gap among them.…”

Section: B Multi-layer Perceptron For Lane Change Predictionmentioning

confidence: 99%

Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study

Liu

Wang

Han

et al. 2022

IEEE Trans. Intell. Veh.

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With the rapid development of intelligent vehicles and Advanced Driver-Assistance Systems (ADAS), a new trend is that mixed levels of human driver engagements will be involved in the transportation system. Therefore, necessary visual guidance for drivers is vitally important under this situation to prevent potential risks. To advance the development of visual guidance systems, we introduce a novel vision-cloud data fusion methodology, integrating camera image and Digital Twin information from the cloud to help intelligent vehicles make better decisions. Target vehicle bounding box is drawn and matched with the help of the object detector (running on the ego-vehicle) and position information (received from the cloud). The best matching result, a 79.2% accuracy under 0.7 intersection over union threshold, is obtained with depth images served as an additional feature source. A case study on lane change prediction is conducted to show the effectiveness of the proposed data fusion methodology. In the case study, a multi-layer perceptron algorithm is proposed with modified lane change prediction approaches. Human-in-the-loop simulation results obtained from the Unity game engine reveal that the proposed model can improve highway driving performance significantly in terms of safety, comfort, and environmental sustainability.

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Section: B Multi-layer Perceptron For Lane Change Predictionmentioning

confidence: 99%

Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study

Liu

Wang

Han

et al. 2022

IEEE Trans. Intell. Veh.

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“…With real-time data sampling and historical data storage, the Human Digital Twin is able to classify drivers into specific driver types by machine learning algorithms like k-nearest neighbors (KNN), and to provide guidance in a customized or personalized manner [41]. Taking advantage of the data coming from the Vehicle block, the Human Digital Twin can also predict future behaviors of drivers (e.g., lane-change intention [42]) and detect their anomalies [43]. The results of the aforementioned microservices can be applied to third parties such as insurance companies, where they can further build a microservice to set the insurance pricing for different drivers based on their driving behaviors [44].…”

Section: Digital Spacementioning

confidence: 99%

Mobility Digital Twin: Concept, Architecture, Case Study, and Future Challenges

Wang¹

2022

Preprint

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<div>A Digital Twin is a digital replica of a living or non-living physical entity, and this emerging technology has attracted extensive attention from different industries during the past decade. Although a few Digital Twin studies have been conducted in the transportation domain very recently, there is no systematic research with a holistic framework connecting various mobility entities together. In this study, by leveraging both connected vehicle technology and cloud computing, an Mobility Digital Twin (MDT) framework is developed, which consists of three building blocks in the physical space (namely Human, Vehicle, and Traffic), and their associated Digital Twins in the digital space. The cloud architecture is built with Amazon Web Services (AWS) to accommodate the proposed MDT framework and to implement its digital functionalities of storage, modeling, learning, simulation, and prediction. The effectiveness of the MDT framework is shown through the case studies of three digital building blocks with their key microservices: the Human Digital Twin with user management and driver type classification, the Vehicle Digital Twin with cloud-based Advanced Driver-Assistance Systems (ADAS), and the Traffic Digital Twin with traffic flow monitoring and variable speed limit.</div>

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“…Many authors used hidden Markov models and naïve Bayes classifiers to predict the lane changes few seconds before they occur [46,49,50,51,52]. Generally speaking, non-linear algorithms by artificial neural networks [53,54,55] and especially deep learning including feedback mechanisms (e.g., long short term memory (LSTM) algorithm) [19,21,56,57,58] provide the most accurate predictions. More recent approaches rely on ensemble learning meta-heuristics combining several algorithms for the prediction.…”

Section: Data-based Algorithmsmentioning

confidence: 99%

Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms

Khelfa¹,

Ba²,

Tordeux³

2022

Preprint

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Understanding and predicting highway lane-change maneuvers is essential for driving modeling and its automation. The development of data-based lane-changing decision-making algorithms is nowadays in full expansion. We compare empirically in this article different machine and ensemble learning classification techniques to the MOBIL rule-based model using trajectory data of European two-lane highways. The analysis relies on instantaneous measurements of up to twenty-four spatial-temporal variables with the four neighboring vehicles on current and adjacent lanes. Preliminary descriptive investigations by principal component and logistic analyses allow identifying main variables intending a driver to change lanes. We predict two types of discretionary lanechange maneuvers: Overtaking (from slow to fast lane) and fold-down (from fast to slow lane). The prediction accuracy is quantified using total, lane-changing and lane-keeping errors and associated receiver operating characteristic curves. The benchmark analysis includes logistic model, linear discriminant, decision tree, naïve Bayes classifier, support vector machine, neural network machine learning algorithms, and up to ten bagging and stacking ensemble learning meta-heuristics. If the rule-based model provides limited predicting accuracy, especially in case of fold-down, the data-based algorithms, devoid of modeling bias, allow significant prediction improvements. Cross validations show that selected neural networks and stacking algorithms allow predicting from a single observation both fold-down and overtaking maneuvers up to four seconds in advance with high accuracy.

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Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron

Cited by 34 publications

References 20 publications

Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study

Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study

Mobility Digital Twin: Concept, Architecture, Case Study, and Future Challenges

Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms

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