Multi-Feature-Based Integrated Vehicle Trajectory Prediction with Driving Intention Recognition

In vehicle trajectory prediction, constructing the interactive relationships among vehicles within the traffic environment poses a significant challenge. Existing models predominantly focus on temporal dependencies within vehicle histories and spatial correlations among neighboring vehicles, overlooking the continuous influence of historical vehicle states on the current time step and the interplay of multiple sequences over time. To address these limitations, we propose a method for multimodal vehicle trajectory prediction that integrates Hybrid Time-step Scene Interaction (HTSI) into the spatiotemporal relationships. Firstly, we introduce the HTSI module, comprising Multi-step Temporal Information Aggregation (MTIA) and Single-step Temporal Information Aggregation (STIA) methods. MTIA utilizes multi-head attention mechanisms to capture temporal dependencies between consecutive frames, thereby generating new time series amalgamating the ongoing influence of historical time states on the current timestamp. Simultaneously, STIA employs multi-head attention mechanisms to capture the spatial dimension weights of multiple time series and, by aggregating spatial interaction features at each timestamp, generates new time series fused with spatial interaction influences. Subsequently, feature extraction is performed through LSTM layers. Moreover, we propose an improved DIPM pooling module, improving the model’s long-term prediction capability by selectively reusing historical hidden states. Ultimately, based on training results from the HighD and NGSIM datasets, our model demonstrates significant advantages in long-term prediction compared to other state-of-the-art trajectory prediction models. Specifically, within the 5 s prediction window, the model achieved a root mean square error (RMSE) of 2.79 m on the NGSIM dataset, representing a 33.62% improvement over the baseline model’s average accuracy. Additionally, on the HighD dataset, the model attained an RMSE of 2.16 m, reflecting a 33.43% enhancement. The crucial code can be obtained from the provided link: https://github.com/gyhhq/Prediction-trajectory .

Vehicle trajectory information is a crucial part of improving the efficiency and the safety of the ITS. Data missing or irregular sampling in the real-world road traffic makes it hard to obtain accurate real-time vehicle trajectories. In this paper, we focus on trajectory imputation and prediction tasks with small data (magnitude set as 10 1 and 10 2 ). Limited by insufficient data, the simulation results of the existing data-driven algorithms are unsatisfactory. With car-following models integrated as prior physical information to constrain the training process, we design the car-following-informed neural network (CFINN). A multi-head self-attention layer is attached to the fully connected network layer to extract vehicle features. Different from the structure of most neural networks in regression analysis, an extra physics-based dataset is constructed in the CFINN. The loss function consists of two parts including the given trajectory's fitting error and the generated trajectory's residual error. We embed the gated recurrent unit-based encoder–decoder layer to the CFINN framework for trajectory predictions. The rationality and the superiority of our model are validated on the NGSIM dataset and the HighD dataset. Compared with baseline models in both single-vehicle and queue-typed trajectory imputation experiments, lower error can be achieved via the CFINN and coefficients of car-following models can be calibrated. According to driving regimes derived from CFINN-based trajectory prediction experiments, we discuss the impact of cut-in behaviours on the target vehicle and carry out the kinetic analysis. The novel neural network model driven by both data and physical knowledge provides technical support in vehicle status assessments and trajectory predictions.

Vehicles' trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services.

Objectives With the continuous advancement of autonomous driving technology, accurate vehicle trajectory prediction plays a crucial role in preventing potential traffic collisions. To address the limitations of existing methods—specifically the insufficient exploitation of historical trajectory information and the inaccurate modeling of multi-vehicle interactions—this study proposes a method that combines multi-scale feature extraction with trajectory optimization, enabling precise prediction of target vehicle trajectories. Methods Targeting urban roads and highway scenarios with naturalistic driving data, surrounding interactive vehicles are first identified using the Pearson correlation coefficient, and historical feature inputs are selected via the Maximal Information Coefficient (MIC). The proposed CTS-Informer model integrates CNN and TCN decoders to capture local and long-range spatiotemporal dependencies from historical trajectories. It further leverages Informer’s sparse attention and self-attention distillation to model global motion trends. Finally, a trajectory correction and smoothing module refines the output for enhanced continuity and realism in the predicted target vehicle trajectory. Results The proposed model was validated on the NGSIM dataset, demonstrating superior performance over existing baseline methods. For a 5-s prediction horizon, the model achieved a 25–48% reduction in ADE and a 12–42% reduction in RMSE. Component ablation experiments further showed that removing the CNN, TCN, or trajectory correction and smoothing module led to a performance drop of 12.8–32.6%, highlighting the contribution of each module to overall accuracy. Conclusions The study demonstrates that the CTS-Informer model delivers strong long-term prediction performance, with each component contributing effectively to overall accuracy. The results validate the effectiveness of multi-scale spatiotemporal feature fusion for extended-horizon prediction. Furthermore, the model provides reliable trajectory boundaries, providing robust reference input for autonomous driving decision-making systems.

Driving intention recognition is an important aspect of Advanced Driving Assistance Systems (ADAS) for giving drivers suggestions to maneuver safely. The intention recognition algorithms in ADAS are often developed using Machine Learning-based models. The model's input, such as environmental (ENV) and eye-tracking (ET) features affect the model's recognition performance. In this contribution, an Artificial Neural Network-based state machine is used for lane changing intention recognition. Three lane changing behaviors are considered, left/right lane change and lane keeping. Here, data consisting of ENV and ET information are collected using a driving simulator and eye-tracker. The aim is to investigate the effect of different feature types on the model's intention recognition performance. First, a 10-cross validation is performed to evaluate the model's performance, using only ENV and both ENV and ET features. The validation results show that the model with only ENV features performs better with respect to different metrics. Thus in the test, only ENV features are used to evaluate the performance. Accuracy values of higher than 80 % are achieved. Furthermore, the recognition performance of the model is compared with other Machine Learning models. The approach introduced outperforms other models in most metrics.

Driver behavior and intention recognition affects traffic safety. Many scholars use the steering wheel angle, distance of the brake pedal, distance of the accelerator pedal, and turn signal as input data to identify driver behaviors and intentions. However, in terms of time, the acquisition of these parameters has a relative delay, which lengthens the identification time. Therefore, this study uses drivers’ EEG (electroencephalograph) data as input parameters to identify driver behaviors and intentions. The key to the driving intention recognition of EEG signals is to reduce their noise. Noise interference has a significant influence on EEG driving intention recognition. To substantially denoise EEG signals, this study selects wavelet transform theory and wavelet packet transform technology, collects the EEG signals during driving, uses the threshold noise reduction method on EEG signals to reduce noise, and achieves noise reduction through wavelet packet reconstruction. After the wavelet packet coefficients of EEG signals are obtained, the energy characteristics of the wavelet packet coefficients are extracted as input to the Bayesian theoretical model for driver behavior and intention recognition. Results show that the maximum recognition rate of the Bayesian theoretical model reaches 82.6%. Early driver behavior and intention recognition has important research significance for traffic safety and sustainable traffic development.

Current vehicle trajectory prediction methods often fail to capture the complex spatial and temporal interactions among surrounding vehicles in dynamic environments, leading to suboptimal prediction accuracy. In response to these challenges, we propose a novel Transformer-based model for vehicle trajectory prediction, denoted as the Dynamic Spatial-Temporal Interaction-aware Transformer Network (DSTIFormer). The three main parts of the DSTIFormer architecture are a spatial-temporal embedding module, a spatial-temporal dynamics interaction-aware module, and a trajectory encoderdecoder module. The spatial-temporal embedding module aims to encode vehicular trajectory interactions into latent representations that simultaneously preserve spatial dependencies and temporal features. The second module utilizes a temporal transformer network (TTN) along with a spatial graph transformer network (STN) to capture the dynamic spatial-temporal interactions between vehicles. To make trajectory predictions more accurate, the third module uses a sequence-to-sequence (Seq2Seq) architecture as the encoder and an attention-based decoder layer to effectively extract the long-range sequential dependencies in the process. The model's prediction accuracy with the root mean squared error (RMSE) metric on two publicly available datasets: NGSIM and HighD, and the results demonstrate that our proposed model outperforms several benchmark methods in long-term prediction.

Accurate trajectory prediction of surrounding road users is critical to autonomous driving systems. In mixed traffic flows, road users with different kinds of behaviors and styles bring complexity to the environment, which requires considering interactions among road users when anticipating their future trajectories. This paper presents a long-term interactive trajectory prediction method for surrounding vehicles using a hierarchical multi-sequence learning network. In contrast to non-interactive method which assumes that road users are independent of each other, this method can automatically learn high-level dependencies among multiple interacting vehicles through the proposed structural-LSTM (long short-term memory) network. Specifically, structural-LSTM first assigns one LSTM for each interacting vehicle. Then these LSTMs share their cell states and hidden states with their spatial-neighboring LSTMs by a radial connection, and recurrently analyze the output state of itself as well as the other LSTMs in a deeper layer. Finally based on all output states, the network predicts trajectories for surrounding vehicles. The proposed method is evaluated on the NGSIM dataset, and its results show that satisfyingly accurate prediction performance of long-term trajectories of surrounding vehicles is accessible, e.g., longitudinal and lateral RMS error can be reduced to less than 1.93m and 0.31m over 5s time horizon, respectively.

Background: Today, self-driving cars are already on the roads. However, driving safety remains a huge challenge. Trajectory prediction of traffic targets is one of the important tasks of an autonomous driving environment perception system, and its output trajectory can provide necessary information for decision control and path planning. Although there are many patents and articles related to trajectory prediction, the accuracy of trajectory prediction still needs to be improved. Objective: This paper aimed to propose a novel scheme that considers multi-feature independent encoding trajectory prediction (MFIE). Methods: MFIE is an independently coded trajectory prediction algorithm that consists of a spacetime interaction module and trajectory prediction module, and considers speed characteristics and road characteristics. In the spatiotemporal interaction module, an undirected and weightless static traffic graph is used to represent the interaction between vehicles, and multiple graph convolution blocks are used to perform data mining on the historical information of target vehicles, capture temporal features, and process spatial interaction features. In the trajectory prediction module, three long short-term memory (LSTM) encoders are used to encode the trajectory feature, motion feature, and road constraint feature independently. The three hidden features are spliced into a tensor, and the LSTM decoder is used to predict the future trajectory. Results: On datasets, such as Apollo and NGSIM, the proposed method has shown lower prediction error than traditional model-driven and data-driven methods, and predicted more target vehicles at the same time. It can provide a basis for vehicle path planning on highways and urban roads, and it is of great significance to the safety of autonomous driving. Conclusion: This paper has proposed a multi-feature independent encoders’ trajectory prediction data-driven algorithm, and the effectiveness of the algorithm is verified with a public dataset. The trajectory prediction algorithm considering multi-feature independent encoders provides some reference value for decision planning.

Vehicle trajectory prediction is an important research basis for the decision making and path planning of the intelligent and connected vehicle. In the connected vehicle environment, vehicles share information and drive cooperatively, and the intelligent and connected vehicles are able to obtain more accurate and rich perception information, which provides a data basis for accurate prediction of vehicle trajectories. However, attaining accurate and effective vehicle trajectory predictions poses technical challenges due to insufficient extraction of vehicular spatial–temporal interaction features. In this paper, we propose a vehicle trajectory prediction model based on graph convolutional neural network (GCN) in a connected vehicle environment. Specifically, using the driving scene information obtained by the intelligent and connected vehicle, the spatial graph and temporal graph are constructed based on the spatial interaction coefficient (SIC) and self-attention mechanism, respectively. Then, the graph data are entered into the interaction extraction module, and the spatial interaction features and temporal interaction features are extracted separately using the graph convolutional networks, which are fused to obtain the spatial–temporal interaction information. Finally, the interaction features are learned based on the convolutional neural networks to output the future trajectory information of all vehicles in the scene by one forward operation rather than a step-by-step process. The ablation experiment results show that the method proposed in this study to model the spatiotemporal interaction among vehicles based on SIC and self-attention mechanism reduces the prediction error by 5% and 12%, respectively. The results from the model comparison experiment show that the proposed method engenders an 8% improvement in prediction accuracy over the state-of-the-art solution, providing technical and theoretical support for trajectory prediction research of intelligent and connected vehicles.

Pedestrian trajectory prediction is a critical research area with numerous domains, e.g., blind navigation, autonomous driving systems, and service robots. There exist two challenges in this research field: spatio-temporal interaction modeling among pedestrians and the uncertainty of pedestrian trajectories. To tackle these challenges, we propose a spatio-temporal interaction aware and trajectory distribution aware graph convolution network. First, we propose a spatio-temporal interaction aware module that integrates a graph convolutional network and self-attention mechanism to model spatio-temporal interactions among pedestrians. Second, we design a trajectory distribution aware module to learn latent trajectory distribution information from the measured trajectories at observed and future times. This can provide knowledge-rich trajectory distribution information for the multimodality of the predicted trajectories. Finally, to address the problem of the propagation and accumulation of prediction errors, we design a trajectory decoder to generate the multimodal future trajectories. The proposed model is evaluated utilizing videos recorded by a camera sensor in crowded areas and can be applied to predict multiple pedestrians’ future trajectories from in-vehicle cameras. Experimental results demonstrate that the proposed approach can achieve superior results on the average displacement error (ADE) and final displacement error (FDE) metrics to state-of-the-art approaches and can predict socially acceptable future trajectories.

Pedestrian trajectory prediction is fundamental to a wide range of scientific research work and industrial applications. Most of the current advanced trajectory prediction methods incorporate context information such as pedestrian neighbourhood, labelled static obstacles, and the background scene into the trajectory prediction process. In contrast to these methods which require rich contexts, the method in our paper focuses on predicting a pedestrian's future trajectory using his/her observed part of the trajectory only. Our method, which we refer to as LVTA, is a Location-Velocity-Temporal Attention LSTM model where two temporal attention mechanisms are applied to the hidden state vectors from the location and velocity LSTM layers. In addition, a location-velocity attention layer embedded inside a tweak module is used to improve the predicted location and velocity coordinates before they are passed to the next time step. Extensive experiments conducted on three large benchmark datasets and comparison with eleven existing trajectory prediction methods demonstrate that LVTA achieves competitive prediction performance. Specifically, LVTA attains 9.19 pixels Average Displacement Error (ADE) and 17.28 pixels Final Displacement Error (FDE) for the Central Station dataset, and 0.46 metres ADE and 0.92 metres FDE for the ETH&UCY datasets. Furthermore, evaluation on using LVTA to generate trajectories of different prediction lengths and on new scenes without the need of retraining confirms that it has good generalizability.

For autonomous driving, accurate trajectory prediction is paramount, necessitating effective harnessing of spatiotemporal data. This study proposes an innovative Spatiotemporal Transformer-based model, enhancing trajectory prediction precision by leveraging a multi-head self-attention mechanism. This mechanism intricately captures both inter-vehicular interactions and temporal trajectory dependencies. The model, structured around an LSTM-based encoder-decoder framework, innovatively considers spatial interactions among observed and future trajectories during the decoding process. Evaluated on the NGSIM dataset, our model demonstrates superior predictive accuracy over existing methodologies, underscoring the efficacy of Spatiotemporal Transformers in complex dynamic environments.

Forecasting pedestrian trajectories in dynamic scenes remains a critical problem in various applications, such as autonomous driving and socially aware robots. Such forecasting is challenging due to human-human and human-object interactions and future uncertainties caused by human randomness. Generative model-based methods handle future uncertainties by sampling a latent variable. However, few studies explored the generation of the latent variable. In this work, we propose the trajectory predictor with pseudo Oracle (TPPO), which is a generative model-based trajectory predictor. The first pseudo oracle is pedestrians’ moving directions, and the second one is the latent variable estimated from ground truth trajectories. A social attention module is used to aggregate neighbors’ interactions based on the correlation between pedestrians’ moving directions and future trajectories. This correlation is inspired by the fact that pedestrians’ future trajectories are often influenced by pedestrians in front. A latent variable predictor is proposed to estimate latent variable distributions from observed and ground-truth trajectories. Moreover, the gap between these two distributions is minimized during training. Therefore, the latent variable predictor can estimate the latent variable from observed trajectories to approximate that estimated from ground-truth trajectories. We compare the performance of TPPO with related methods on several public datasets. Results demonstrate that TPPO outperforms state-of-the-art methods with low average and final displacement errors. The ablation study shows that the prediction performance will not dramatically decrease as sampling times decline during tests.

Trajectory prediction for challenging scenarios has always been a significant problem in the field due to the complexity of dynamic scenarios and interactions. Furthermore, there is often a dynamic gap between evaluating and validating methods on fixed datasets and real driving scenarios. This letter forms part of a series of reports emanating from the IEEE Transactions on Intelligent Vehicles's Decentralized and Hybrid Workshops (DHW) dedicated to the field of Scenarios Engineering. Our research proposes a scenario engineering-based calibration and validation framework for trajectory prediction of autonomous vehicles to more effectively validate the performance of the method in challenging scenarios. First, Scenarios Engineering (SE) uses OpenSCENARIO and real dataset to generate high-definition maps for challenging driving scenarios. Then, the vectorization approach is employed to extract contextual details from the scene and agent trajectory information from the HD map, and the graph neural network is used to model the high-order interaction to realize the interactive trajectory prediction. Compared with the traditional method, the trajectory prediction can be calibrated through SE so that the prediction process can use more traffic information and attribute characteristics, and improve the evaluation index of prediction. The DHW discusses a practical case to verify the potential of the trajectory prediction framework based on scenarios generation in improving the authenticity and accuracy of trajectory prediction.