A Bayesian inference based adaptive lane change prediction model

2021 ◽  
Vol 132 ◽  
pp. 103363
Author(s):  
Jinghua Wang ◽  
Zhao Zhang ◽  
Guangquan Lu
Keyword(s):  
Bayesian Inference ◽  
Prediction Model ◽  
Lane Change ◽  
Change Prediction

scholarly journals Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Christina Ng ◽  
Susilawati Susilawati ◽  
Md Abdus Samad Kamal ◽  
Irene Mei Leng Chew
Keyword(s):  
Prediction Model ◽  
Transmission Model ◽  
Lane Change ◽  
Traffic Data ◽  
Cell Transmission Model ◽  
Traffic State ◽  
Proposed Model ◽  
Significant Difference ◽  
The Mean ◽  
Change Prediction

This paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation. To achieve these objectives, first, based on the observed traffic data, the binary logistic lane change model is developed to formulate the lane change occurrence. Second, the binary logistic lane change is integrated into CTM by refining CTM formulations on how the vehicles in the cell are moving from one cell to another in a longitudinal manner and how cell occupancy is updated after lane change occurrences. The performance of the proposed model is evaluated by comparing the simulated cell occupancy of the proposed model with cell occupancy of US-101 next generation simulation (NGSIM) data. The results indicated no significant difference between the mean of the cell occupancies of the proposed model and the mean of cell occupancies of actual data with a root-mean-square-error (RMSE) of 0.04. Similar results are found when the proposed model was further tested with I80 highway data. It is suggested that the mean of cell occupancies of I80 highway data was not different from the mean of cell occupancies of the proposed model with 0.074 RMSE (0.3 on average).

Machine Learning Approach for Predicting Lane-Change Maneuvers using the SHRP2 Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110035
Author(s):  
Anik Das ◽  
Mohamed M. Ahmed
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lane Change ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Change Prediction

Accurate lane-change prediction information in real time is essential to safely operate Autonomous Vehicles (AVs) on the roadways, especially at the early stage of AVs deployment, where there will be an interaction between AVs and human-driven vehicles. This study proposed reliable lane-change prediction models considering features from vehicle kinematics, machine vision, driver, and roadway geometric characteristics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. Several machine learning algorithms were trained, validated, tested, and comparatively analyzed including, Classification And Regression Trees (CART), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naïve Bayes (NB) based on six different sets of features. In each feature set, relevant features were extracted through a wrapper-based algorithm named Boruta. The results showed that the XGBoost model outperformed all other models in relation to its highest overall prediction accuracy (97%) and F1-score (95.5%) considering all features. However, the highest overall prediction accuracy of 97.3% and F1-score of 95.9% were observed in the XGBoost model based on vehicle kinematics features. Moreover, it was found that XGBoost was the only model that achieved a reliable and balanced prediction performance across all six feature sets. Furthermore, a simplified XGBoost model was developed for each feature set considering the practical implementation of the model. The proposed prediction model could help in trajectory planning for AVs and could be used to develop more reliable advanced driver assistance systems (ADAS) in a cooperative connected and automated vehicle environment.

scholarly journals Attention-based Lane Change Prediction

2019 ◽  
Author(s):  
Oliver Scheel ◽  
Naveen Shankar Nagaraja ◽  
Loren Schwarz ◽  
Nassir Navab ◽  
Federico Tombari
Keyword(s):  
Lane Change ◽  
Change Prediction

scholarly journals Reducing Noise in Label Annotation: A Lane Change Prediction Case Study

2019 ◽  
Vol 52 (8) ◽  
pp. 221-226 ◽  
Author(s):  
Martin Krüger ◽  
Anne Stockem Novo ◽  
Till Nattermann ◽  
Manoj Mohamed ◽  
Torsten Bertram
Keyword(s):  
Lane Change ◽  
Change Prediction

A hybrid model for lane change prediction with V2X-based driver assistance

2019 ◽  
Vol 534 ◽  
pp. 122033 ◽  
Author(s):  
Ting Xu ◽  
Ruisen Jiang ◽  
Changlei Wen ◽  
Meijun Liu ◽  
Jiehan Zhou
Keyword(s):  
Hybrid Model ◽  
Lane Change ◽  
Driver Assistance ◽  
Change Prediction

Lane Change Prediction based on Trajectory Analysis and Driver Actions Tracking

2021 ◽  
Author(s):  
Anish Dixit ◽  
Shreya Oak ◽  
Shaney Mantri ◽  
Anant Nimkar ◽  
Meghana Naik
Keyword(s):  
Trajectory Analysis ◽  
Lane Change ◽  
Change Prediction

Lane Change Prediction: From Driver Characteristics, Manoeuvre Types and Glance Behaviour to a Real-Time Prediction Algorithm

2017 ◽  
pp. 205-221 ◽  
Author(s):  
Matthias Beggiato ◽  
Timo Pech ◽  
Veit Leonhardt ◽  
Philipp Lindner ◽  
Gerd Wanielik ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Algorithm ◽  
Lane Change ◽  
Time Prediction ◽  
Change Prediction
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