scholarly journals Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment

2021 ◽  
Vol 33 (5) ◽  
pp. 767-774
Author(s):  
Pengfei Liu ◽  
Wei Fan
Keyword(s):  
Autonomous Vehicles ◽  
Driver Model ◽  
Current Status ◽  
Gradient Boosting ◽  
Prediction Errors ◽  
Trajectory Prediction ◽  
Acceleration Rate ◽  
Extreme Gradient Boosting ◽  
Vehicle Trajectory ◽  
Vehicle Trajectory Prediction

Connected and autonomous vehicles (CAVs) have the ability to receive information on their leading vehicles through multiple sensors and vehicle-to-vehicle (V2V) technology and then predict their future behaviour thus to improve roadway safety and mobility. This study presents an innovative algorithm for connected and autonomous vehicles to determine their trajectory considering surrounding vehicles. For the first time, the XGBoost model is developed to predict the acceleration rate that the object vehicle should take based on the current status of both the object vehicle and its leading vehicle. Next Generation Simulation (NGSIM) datasets are utilised for training the proposed model. The XGBoost model is compared with the Intelligent Driver Model (IDM), which is a prior state-of-the-art model. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are applied to evaluate the two models. The results show that the XGBoost model outperforms the IDM in terms of prediction errors. The analysis of the feature importance reveals that the longitudinal position has the greatest influence on vehicle trajectory prediction results.

scholarly journals Vehicle Trajectory Prediction Using Hierarchical Graph Neural Network for Considering Interaction among Multimodal Maneuvers

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5354
Author(s):  
Eunsan Jo ◽  
Myoungho Sunwoo ◽  
Minchul Lee
Keyword(s):  
Neural Network ◽  
Autonomous Vehicles ◽  
Prediction Performance ◽  
Data Driven ◽  
Graph Structure ◽  
Trajectory Prediction ◽  
Hierarchical Graph ◽  
Vehicle Trajectory ◽  
Using Data ◽  
Vehicle Trajectory Prediction

Predicting the trajectories of surrounding vehicles by considering their interactions is an essential ability for the functioning of autonomous vehicles. The subsequent movement of a vehicle is decided based on the multiple maneuvers of surrounding vehicles. Therefore, to predict the trajectories of surrounding vehicles, interactions among multiple maneuvers should be considered. Recent research has taken into account interactions that are difficult to express mathematically using data-driven deep learning methods. However, previous studies have only considered the interactions among observed trajectories due to subsequent maneuvers that are unobservable and numerous maneuver combinations. Thus, to consider the interaction among multiple maneuvers, this paper proposes a hierarchical graph neural network. The proposed hierarchical model approximately predicts the multiple maneuvers of vehicles and considers the interaction among the maneuvers by representing their relationships in a graph structure. The proposed method was evaluated using a publicly available dataset and a real driving dataset. Compared with previous methods, the results of the proposed method exhibited better prediction performance in highly interactive situations.

scholarly journals Probabilistic Bounds on Vehicle Trajectory Prediction Using Scenario Approach

2020 ◽  
Vol 53 (2) ◽  
pp. 2385-2390
Author(s):  
Xun Shen ◽  
Xingguo Zhang ◽  
Pongsathorn Raksincharoensak
Keyword(s):  
Trajectory Prediction ◽  
Vehicle Trajectory ◽  
Vehicle Trajectory Prediction ◽  
Scenario Approach

Driver influence on vehicle trajectory prediction

2021 ◽  
Vol 157 ◽  
pp. 106165
Author(s):  
Mahrokh Khakzar ◽  
Andy Bond ◽  
Andry Rakotonirainy ◽  
Oscar Oviedo Trespalacios ◽  
Sepehr G. Dehkordi
Keyword(s):  
Trajectory Prediction ◽  
Vehicle Trajectory ◽  
Vehicle Trajectory Prediction

scholarly journals Exploring the Mechanism of Crashes with Autonomous Vehicles Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hengrui Chen ◽  
Hong Chen ◽  
Ruiyu Zhou ◽  
Zhizhen Liu ◽  
Xiaoke Sun
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Crash Severity ◽  
Apriori Algorithm ◽  
Driving Mode ◽  
Extreme Gradient Boosting ◽  
The Impact

The safety issue has become a critical obstacle that cannot be ignored in the marketization of autonomous vehicles (AVs). The objective of this study is to explore the mechanism of AV-involved crashes and analyze the impact of each feature on crash severity. We use the Apriori algorithm to explore the causal relationship between multiple factors to explore the mechanism of crashes. We use various machine learning models, including support vector machine (SVM), classification and regression tree (CART), and eXtreme Gradient Boosting (XGBoost), to analyze the crash severity. Besides, we apply the Shapley Additive Explanations (SHAP) to interpret the importance of each factor. The results indicate that XGBoost obtains the best result (recall = 75%; G-mean = 67.82%). Both XGBoost and Apriori algorithm effectively provided meaningful insights about AV-involved crash characteristics and their relationship. Among all these features, vehicle damage, weather conditions, accident location, and driving mode are the most critical features. We found that most rear-end crashes are conventional vehicles bumping into the rear of AVs. Drivers should be extremely cautious when driving in fog, snow, and insufficient light. Besides, drivers should be careful when driving near intersections, especially in the autonomous driving mode.

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