Recurrent Models for Lane Change Prediction and Situation Assessment

Scheel, Oliver; Nagaraja, Naveen Shankar; Schwarz, Loren; Navab, Nassir; Tombari, Federico

doi:10.1109/tits.2022.3163353

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…It automatically learns to selectively focus on the most relevant information for the current task, dynamically attending to different segments of the input sequence and extracting the most informative segments. This enhances the model's ability to handle crucial information and improves the performance in recognizing LC risk [59].…”

Section: Cnn-bilstm-attentionmentioning

confidence: 98%

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A Comprehensive Investigation of Lane-Changing Risk Recognition Framework of Multi-Vehicle Type Considering Key Features Based on Vehicles’ Trajectory Data

Zheng,

Liu

2024

Electronics

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To comprehensively investigate the key features of lane-changing (LC) risk for different vehicle types during left and right LC, and to improve the accuracy of LC risk recognition, this paper proposes a key feature selection and risk recognition model based on vehicle trajectory data. Based on a HighD high-precision vehicle trajectory dataset, the trajectory data of LC vehicles and surrounding vehicles of each vehicle type are extracted. SDI (stop distance index) and CI (crash index) are selected as surrogate indicators to calculate the risk exposure level (REL) and risk severity level (RSL). The K-means algorithm is used to cluster the REL and RSL to obtain the LC risk level, which is divided into three levels. The combination of basic features and interaction features of LC vehicles and surrounding vehicles with LC risk levels is constructed as the LC risk feature dataset. Based on the LightGBM (light gradient boosting machine) algorithm, the importance of features is sorted. Finally, a CNN-BiLSTM-Attention model is established to recognize the LC risk of each vehicle type during left and right LC. The results indicate that significant differences exist among different vehicle types and LC directions. Compared with CNNs (convolutional neural networks), LSTM (long short-term memory), and BiLSTM (bi-directional long short-term memory), CNN-BiLSTM-Attention performs best in recognizing the risk of LC in all cases. Moreover, the key feature groups that have the optimal result of recognizing the risk of LC in different cases are obtained.

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Section: Cnn-bilstm-attentionmentioning

confidence: 98%

“…13, x FOR PEER REVIEW 13 of 25 the model's ability to handle crucial information and improves the performance in recognizing LC risk[59].…”

mentioning

confidence: 99%

A Comprehensive Investigation of Lane-Changing Risk Recognition Framework of Multi-Vehicle Type Considering Key Features Based on Vehicles’ Trajectory Data

Zheng,

Liu

2024

Electronics

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“…W ITH the rapid development of computer science and electrical engineering, autonomous systems have begun to play an increasingly important role in our daily life [1]- [3]. Among them, autonomous driving is undoubtedly one of the most important research fields [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Lane Transformer: A High-Efficiency Trajectory Prediction Model

Wang

Guo

Hu³

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Trajectory prediction is a crucial step in the pipeline for autonomous driving because it not only improves the planning of future routes, but also ensures vehicle safety. On the basis of deep neural networks, numerous trajectory prediction models have been proposed and have already achieved high performance on public datasets due to the well-designed model structure and complex optimization procedure. However, the majority of these methods overlook the fact that vehicles' limited computing resources can be utilized for online real-time inference. We proposed a Lane Transformer to achieve high accuracy and efficiency in trajectory prediction to tackle this problem. On the one hand, inspired by the well-known transformer, we use attention blocks to replace the commonly used Graph Convolution Network (GCN) in trajectory prediction models, thereby drastically reducing the time cost while maintaining the accuracy. In contrast, we construct our prediction model to be compatible with TensorRT, allowing it to be further optimized and easily transformed into a deployment-friendly form of TensorRT. Experiments demonstrate that our model outperforms the baseline LaneGCN model in quantitative prediction accuracy on the Argoverse dataset by a factor of 10× to 25×. Our 7ms inference time is the fastest among all open source methods currently available. Our code is publicly available at: https://github.com/mmdzb/Lane-Transformer.

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Introduction

2024

Stochastic Two-Dimensional Microscopic Traffic Model

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Recurrent Models for Lane Change Prediction and Situation Assessment

Cited by 7 publications

References 37 publications

A Comprehensive Investigation of Lane-Changing Risk Recognition Framework of Multi-Vehicle Type Considering Key Features Based on Vehicles’ Trajectory Data

A Comprehensive Investigation of Lane-Changing Risk Recognition Framework of Multi-Vehicle Type Considering Key Features Based on Vehicles’ Trajectory Data

Lane Transformer: A High-Efficiency Trajectory Prediction Model

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