Prediction of Lane Change Intentions of Human Drivers using an LSTM, a CNN and a Transformer

• URL: http://arxiv.org/abs/2507.08365v1
• Date: Fri, 11 Jul 2025 07:26:33 GMT
• Title: Prediction of Lane Change Intentions of Human Drivers using an LSTM, a CNN and a Transformer
• Authors: Francesco De Cristofaro, Felix Hofbaur, Aixi Yang, Arno Eichberger,
• Abstract summary: Lane changes of preceding vehicles have a great impact on the motion planning of automated vehicles.<n>In this paper the structure of an LSTM, a CNN and a Transformer network are described and implemented to predict the intention of human drivers to perform a lane change.<n>The accuracy of the method spanned from $82.79%$ to $96.73%$ for different input configurations and showed overall good performances considering also precision and recall.
• Score: 0.33748750222488655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lane changes of preceding vehicles have a great impact on the motion planning of automated vehicles especially in complex traffic situations. Predicting them would benefit the public in terms of safety and efficiency. While many research efforts have been made in this direction, few concentrated on predicting maneuvers within a set time interval compared to predicting at a set prediction time. In addition, there exist a lack of comparisons between different architectures to try to determine the best performing one and to assess how to correctly choose the input for such models. In this paper the structure of an LSTM, a CNN and a Transformer network are described and implemented to predict the intention of human drivers to perform a lane change. We show how the data was prepared starting from a publicly available dataset (highD), which features were used, how the networks were designed and finally we compare the results of the three networks with different configurations of input data. We found that transformer networks performed better than the other networks and was less affected by overfitting. The accuracy of the method spanned from $82.79\%$ to $96.73\%$ for different input configurations and showed overall good performances considering also precision and recall.

Related papers

• Overtake Detection in Trucks Using CAN Bus Signals: A Comparative Study of Machine Learning Methods [51.28632782308621]
  We focus on overtake detection using Controller Area Network (CAN) bus data collected from five in-service trucks provided by the Volvo Group.<n>We evaluate three common classifiers for vehicle manoeuvre detection, Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM)<n>Our pertruck analysis also reveals that classification accuracy, especially for overtakes, depends on the amount of training data per vehicle.
  arXiv  Detail & Related papers  (2025-07-01T09:20:41Z)
• A Dynamic Transformer Network for Vehicle Detection [57.4144097001218]
  We present a dynamic Transformer network for vehicle detection (DTNet)<n>DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector.<n>To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection.
  arXiv  Detail & Related papers  (2025-06-03T11:29:35Z)
• Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction [15.454206825258169]
  Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians. This paper proposes a graph transformer structure to improve prediction performance.
  arXiv  Detail & Related papers  (2024-01-10T01:50:29Z)
• Deep Sequence Modeling for Anomalous ISP Traffic Prediction [3.689539481706835]
  We investigated and evaluated the performance of different deep sequence models for anomalous traffic prediction. LSTM_Encoder_Decoder (LSTM_En_De) is the best prediction model in our experiment, reducing the deviation between actual and predicted traffic by more than 11% after adjusting the outliers.
  arXiv  Detail & Related papers  (2022-05-03T17:01:45Z)
• Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms [0.0]
  We compare different machine and ensemble learning classification techniques to the rule-based model. We predict two types of discretionary lane-change maneuvers: Overtaking (from slow to fast lane) and fold-down. If the rule-based model provides limited predicting accuracy, especially in case of fold-down, the data-based algorithms, devoid of modeling bias, allow significant prediction improvements.
  arXiv  Detail & Related papers  (2022-04-20T22:55:59Z)
• Real-time Object Detection for Streaming Perception [84.2559631820007]
  Streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perception. We build a simple and effective framework for streaming perception. Our method achieves competitive performance on Argoverse-HD dataset and improves the AP by 4.9% compared to the strong baseline.
  arXiv  Detail & Related papers  (2022-03-23T11:33:27Z)
• Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks [0.5399800035598186]
  This paper deals with the development of a system that accurately predicts the time to the next lane change of surrounding vehicles on highways. An evaluation based on a large real-world data set shows that our approach is able to make reliable predictions, even in the most challenging situations.
  arXiv  Detail & Related papers  (2021-02-02T11:04:22Z)
• PnPNet: End-to-End Perception and Prediction with Tracking in the Loop [82.97006521937101]
  We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles. We propose Net, an end-to-end model that takes as input sensor data, and outputs at each time step object tracks and their future level.
  arXiv  Detail & Related papers  (2020-05-29T17:57:25Z)
• Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing [0.27528170226206433]
  DistPre is a distributed fine-grained traffic speed prediction scheme for large-scale transportation networks. D DistPre provides time-efficient LSTM customization and accurate fine-grained traffic-speed prediction for large-scale transportation networks.
  arXiv  Detail & Related papers  (2020-05-10T21:24:23Z)
• TPNet: Trajectory Proposal Network for Motion Prediction [81.28716372763128]
  Trajectory Proposal Network (TPNet) is a novel two-stage motion prediction framework. TPNet first generates a candidate set of future trajectories as hypothesis proposals, then makes the final predictions by classifying and refining the proposals. Experiments on four large-scale trajectory prediction datasets, show that TPNet achieves the state-of-the-art results both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2020-04-26T00:01:49Z)
• ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots [65.33650222396078]
  We develop a parking lot environment and collect a dataset of human parking maneuvers. We compare a multi-modal Long Short-Term Memory (LSTM) prediction model and a Convolution Neural Network LSTM (CNN-LSTM) to a physics-based Extended Kalman Filter (EKF) baseline. Our results show that 1) intent can be estimated well (roughly 85% top-1 accuracy and nearly 100% top-3 accuracy with the LSTM and CNN-LSTM model); 2) knowledge of the human driver's intended parking spot has a major impact on predicting parking trajectory; and 3) the semantic representation of the environment
  arXiv  Detail & Related papers  (2020-04-21T20:46:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.