Vehicle Trajectory Prediction and Cut-In Collision Warning Model in a Connected Vehicle Environment

Lyu, Nengchao; Wen, Jiaqiang; Duan, Zhicheng; Wu, Chaozhong

doi:10.1109/tits.2020.3019050

Cited by 59 publications

(23 citation statements)

References 32 publications

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“…In this work, microlevel data, such as the driving track, velocity, and acceleration of the moving target, are obtained using sensor equipment, including light detection and ranging (LiDAR) and cameras. Employing data from research fields that include state estimation [1], intention recognition [2], trajectory prediction [3], intelligent driving [4], driving behavior analysis [5], and safety risk detection [6] can assist intelligent transportation systems in improving traffic safety and reduce accidents.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information

Ruibin

Guo

Long

et al. 2022

Journal of Advanced Transportation

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A vehicle motion state prediction algorithm integrating point cloud timing multiview features and multitarget interaction information is proposed in this work to effectively predict the motion states of traffic participants around intelligent vehicles in complex scenes. The algorithm analyzes the characteristics of object motion that are affected by the surrounding environment and the interaction of nearby objects and is based on the complex traffic environment perception dual multiline light detection and ranging (LiDAR) technology. The time sequence aerial view map and time sequence front view depth map are obtained using real-time point cloud information perceived by the LiDAR. Time sequence high-level abstract combination features in the multiview scene are then extracted by an improved VGG19 network model and are fused with the potential spatiotemporal interaction of the multitarget operation state data extraction features detected by the laser radar by using a one-dimensional convolution neural network. A temporal feature vector is constructed as the input data of the bidirectional long-term and short-term memory (BiLSTM) network, and the desired input-output mapping relationship is trained to predict the motion state of traffic participants. According to the test results, the proposed BiLSTM model based on point cloud multiview and vehicle interaction information is better than other methods in predicting the state of target vehicles. The results can provide support for the research to evaluate the risk of intelligent vehicle operation environment.

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Section: Introductionmentioning

confidence: 99%

Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information

Ruibin

Guo

Long

et al. 2022

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…In addition, the residual dropout module is set to improve the transformer network efficiency. Feed-forward networks are given by Equation (5).…”

Section: Feed-forward Networkmentioning

confidence: 99%

“…Compared to studies of surrounding vehicles trajectory prediction [4], preceding target vehicles (PTVs) should receive more attention, which in turn has a higher possibility of risk to the automated ego-vehicle (EV). Based on the future trajectory of PTVs, the EV can generate a more comfortable and safe path, avoiding or mitigating the risk of collision [5].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Trajectory Prediction of Preceding Target Vehicles in Urban Scenario Using Multi-Sensor Fusion

Zou

Hou

et al. 2022

Sensors

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Preceding vehicles have a significant impact on the safety of the vehicle, whether or not it has the same driving direction as an ego-vehicle. Reliable trajectory prediction of preceding vehicles is crucial for making safer planning. In this paper, we propose a framework for trajectory prediction of preceding target vehicles in an urban scenario using multi-sensor fusion. First, the preceding target vehicles historical trajectory is acquired using LIDAR, camera, and combined inertial navigation system fusion in the dynamic scene. Next, the Savitzky–Golay filter is taken to smooth the vehicle trajectory. Then, two transformer-based networks are built to predict preceding target vehicles’ future trajectory, which are the traditional transformer and the cluster-based transformer. In a traditional transformer, preceding target vehicles trajectories are predicted using velocities in the X-axis and Y-axis. In the cluster-based transformer, the k-means algorithm and transformer are combined to predict trajectory in a high-dimensional space based on classification. Driving data from the real-world environment in Wuhan, China, are collected to train and validate the proposed preceding target vehicles trajectory prediction algorithm in the experiments. The result of the performance analysis confirms that the proposed two transformers methods can effectively predict the trajectory using multi-sensor fusion and cluster-based transformer method can achieve better performance than the traditional transformer.

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“…According to statistics, by the end of 2019, the number of cars in China has reached 220 million, and the number of drivers is about 397 million. The increasing number of motor vehicles and the uneven level of safety awareness of drivers make China's road environment more crowded and complex [ 2 – 4 ]. In 2018 alone, there were as many as 160,000 traffic safety accidents involving vehicles, with about 40,000 deaths and direct economic losses of 1.1 billion yuan [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Based Lane-Changing Behavior Recognition and Lateral Trajectory Prediction

Feng

Yan

2022

Computational Intelligence and Neuroscience

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With the development of technology, vehicle trajectory prediction and safety decision technology has become an important part of active safety technology. Among them, the vehicle trajectory prediction technology can predict the vehicle position, speed, and other motion states in the predicted period according to the current and historical vehicle running state, and the prediction results can provide support for judging the vehicle safety in the predicted period. In order to analyze the above problems, this study fully extracted the main feature information from the vehicle lane change track data with the help of the powerful nonlinear learning and high pattern recognition ability of support vector machine, and conducted identification modeling for the actual lane change process of the vehicle and predictive analysis of the vehicle lateral movement track. First, the lane-changing behavior of vehicles was analyzed, and the vehicle lane-changing execution stage and 10 influencing factors that could characterize lane-changing behavior were determined based on NGSIM to extract the data of lane-change-related variables. Then, a lane-changing recognition model based on gridsearch-PSO is proposed. In the Matlab environment, the model has a test accuracy of 97.68%, while the SVM model without optimization parameters has a recognition accuracy of only 80.87%. The results show that the model has strong classification ability and robustness. Finally, by using the polynomial model for lateral movement trajectory fitting, K-fold cross-validation method is used for fitting polynomial model fitting test.

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Vehicle Trajectory Prediction and Cut-In Collision Warning Model in a Connected Vehicle Environment

Cited by 59 publications

References 32 publications

Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information

Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information

A Framework for Trajectory Prediction of Preceding Target Vehicles in Urban Scenario Using Multi-Sensor Fusion

Support Vector Machine Based Lane-Changing Behavior Recognition and Lateral Trajectory Prediction

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