Development and test of a lane change prediction algorithm for automated driving

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.

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Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model

Journal of Advanced Transportation ◽

10.1155/2021/7905609 ◽

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).

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Attention-based Lane Change Prediction

2019 International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra.2019.8793648 ◽

2019 ◽

Cited By ~ 4

Author(s):

Oliver Scheel ◽

Naveen Shankar Nagaraja ◽

Loren Schwarz ◽

Nassir Navab ◽

Federico Tombari

Keyword(s):

Lane Change ◽

Change Prediction

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Tropical Cyclone Intensity Change Prediction Based on Surrounding Environmental Conditions with Deep Learning

Water ◽

10.3390/w12102685 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2685

Author(s):

Xin Wang ◽

Wenke Wang ◽

Bing Yan

Keyword(s):

Deep Learning ◽

Tropical Cyclone ◽

Environmental Conditions ◽

Environmental Variables ◽

Intensity Change ◽

Prediction Algorithm ◽

Hybrid Features ◽

Distribution Features ◽

Intensity Changes ◽

Change Prediction

Tropical cyclone (TC) motion has an important impact on both human lives and infrastructure. Predicting TC intensity is crucial, especially within the 24 h warning time. TC intensity change prediction can be regarded as a problem of both regression and classification. Statistical forecasting methods based on empirical relationships and traditional numerical prediction methods based on dynamical equations still have difficulty in accurately predicting TC intensity. In this study, a prediction algorithm for TC intensity changes based on deep learning is proposed by exploring the joint spatial features of three-dimensional (3D) environmental conditions that contain the basic variables of the atmosphere and ocean. These features can also be interpreted as fused characteristics of the distributions and interactions of these 3D environmental variables. We adopt a 3D convolutional neural network (3D-CNN) for learning the implicit correlations between the spatial distribution features and TC intensity changes. Image processing technology is also used to enhance the data from a small number of TC samples to generate the training set. Considering the instantaneous 3D status of a TC, we extract deep hybrid features from TC image patterns to predict 24 h intensity changes. Compared to previous studies, the experimental results show that the mean absolute error (MAE) of TC intensity change predictions and the accuracy of the classification as either intensifying or weakening are both significantly improved. The results of combining features of high and low spatial layers confirm that considering the distributions and interactions of 3D environmental variables is conducive to predicting TC intensity changes, thus providing insight into the process of TC evolution.

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