A lane change prediction algorithm based on probabilistic modeling

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

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2019.08.074 ◽

2019 ◽

Vol 52 (8) ◽

pp. 221-226 ◽

Cited By ~ 1

Author(s):

Martin Krüger ◽

Anne Stockem Novo ◽

Till Nattermann ◽

Manoj Mohamed ◽

Torsten Bertram

Keyword(s):

Lane Change ◽

Change Prediction

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A Path Prediction Algorithm of Surrounding Vehicles Based on Sensor Fusion for Safe Lane Change

The Journal of Korean Institute of Communications and Information Sciences ◽

10.7840/kics.2020.45.5.828 ◽

2020 ◽

Vol 45 (5) ◽

pp. 828-836

Author(s):

Jihun Kim ◽

Dong Seog Han

Keyword(s):

Sensor Fusion ◽

Prediction Algorithm ◽

Lane Change ◽

Path Prediction

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Target Vehicle Selection Algorithm for Adaptive Cruise Control Based on Lane-changing Intention of Preceding Vehicle

Chinese Journal of Mechanical Engineering ◽

10.1186/s10033-021-00650-8 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Jun Yao ◽

Guoying Chen ◽

Zhenhai Gao

Keyword(s):

Adaptive Cruise Control ◽

Prediction Algorithm ◽

Support Vector ◽

Lane Change ◽

Selection Algorithm ◽

Cruise Control ◽

Lane Changing ◽

Target Vehicle ◽

Preceding Vehicle ◽

The Subject

AbstractTo improve the ride comfort and safety of a traditional adaptive cruise control (ACC) system when the preceding vehicle changes lanes, it proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention for the preceding vehicle. First, the Next Generation Simulation dataset is used to train a lane-changing intention prediction algorithm based on a sliding window support vector machine, and the lane-changing intention of the preceding vehicle in the current lane is identified by lateral position offset. Second, according to the lane-changing intention and collision threat of the preceding vehicle, the target vehicle selection algorithm is studied under three different conditions: safe lane-changing, dangerous lane-changing, and lane-changing cancellation. Finally, the effectiveness of the proposed algorithm is verified in a co–simulation platform. The simulation results show that the target vehicle selection algorithm can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels their lane change maneuver. In the case of a dangerous lane change, the target vehicle selection algorithm proposed in this paper can respond more rapidly to a dangerous lane change than the target vehicle selection method of the traditional ACC system; thus, it can effectively avoid collisions and improve the safety of the subject vehicle.

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Validating a Traffic Conflict Prediction Technique for Motorways Using a Simulation Approach

Sensors ◽

10.3390/s22020566 ◽

2022 ◽

Vol 22 (2) ◽

pp. 566

Author(s):

Nicolette Formosa ◽

Mohammed Quddus ◽

Alkis Papadoulis ◽

Andrew Timmis

Keyword(s):

Autonomous Vehicles ◽

Safety Management ◽

Management Strategies ◽

Ground Truth ◽

Prediction Algorithm ◽

Lane Change ◽

Prediction Ability ◽

Integrated Simulation ◽

Traffic Conflict ◽

Conflict Prediction

With the ever-increasing advancements in the technology of driver assistant systems, there is a need for a comprehensive way to identify traffic conflicts to avoid collisions. Although significant research efforts have been devoted to traffic conflict techniques applied for junctions, there is dearth of research on these methods for motorways. This paper presents the validation of a traffic conflict prediction algorithm applied to a motorway scenario in a simulated environment. An automatic video analysis system was developed to identify lane change and rear-end conflicts as ground truth. Using these conflicts, the prediction ability of the traffic conflict technique was validated in an integrated simulation framework. This framework consisted of a sub-microscopic simulator, which provided an appropriate testbed to accurately simulate the components of an intelligent vehicle, and a microscopic traffic simulator able to generate the surrounding traffic. Results from this framework show that for a 10% false alarm rate, approximately 80% and 73% of rear-end and lane change conflicts were accurately predicted, respectively. Despite the fact that the algorithm was not trained using the virtual data, the sensitivity was high. This highlights the transferability of the algorithm to similar road networks, providing a benchmark for the identification of traffic conflict and a relevant step for developing safety management strategies for autonomous vehicles.

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