Robust Long-Term Vehicle Trajectory Prediction Using Link Projection and a Situation-Aware Transformer

The trajectory prediction of a vehicle emerges as a pivotal component in Intelligent Transportation Systems. On urban roads where external factors such as intersections and traffic control devices significantly affect driving patterns along with the driver's intrinsic habits, the prediction task becomes much more challenging. Furthermore, long-term forecasting of trajectories accumulates prediction errors, leading to substantially inaccurate predictions that may deviate from the actual road. As a solution to these challenges, we propose a long-term vehicle trajectory prediction method that is robust to error accumulation and prevents off-road predictions. In this study, the Transformer model is utilized to analyze and forecast vehicle trajectories. In addition, we propose an extra encoding network to precisely capture the effect of the external factors on the driving pattern by producing an abstract representation of the situation nearby the driver. To avoid off-road predictions, we propose a post-processing method, called link projection, which projects predictions onto the road geometry. Moreover, to overcome the limitations of Euclidean distance-based evaluation metrics in evaluating the accuracy of the entire trajectory, we propose a new metric called area-between-curves (ABC). It measures the similarity between two trajectories, and thus the accordance between the two can be effectively evaluated. Extensive evaluations are conducted using real-world datasets against widely-used methods to demonstrate the effectiveness of the proposed approach. The results show that the proposed approach outperforms the conventional deep learning models by up to 65.74% (RMSE), 60.13% (MAE) and 91.45% (ABC).

Goal-Guided Graph Attention Network with Interactive State Refinement for Multi-Agent Trajectory Prediction

The accurate prediction of the future trajectories of traffic participants is crucial for enhancing the safety and decision-making capabilities of autonomous vehicles. Modeling social interactions among agents and revealing the inherent relationships is crucial for accurate trajectory prediction. In this context, we propose a goal-guided and interaction-aware state refinement graph attention network (SRGAT) for multi-agent trajectory prediction. This model effectively integrates high-precision map data and dynamic traffic states and captures long-term temporal dependencies through the Transformer network. Based on these dependencies, it generates multiple potential goals and Points of Interest (POIs). Through its dual-branch, multimodal prediction approach, the model not only proposes various plausible future trajectories associated with these POIs, but also rigorously assesses the confidence levels of each trajectory. This goal-oriented strategy enables SRGAT to accurately predict the future movement trajectories of other vehicles in complex traffic scenarios. Tested on the Argoverse and nuScenes datasets, SRGAT surpasses existing algorithms in key performance metrics by adeptly integrating past trajectories and current context. This goal-guided approach not only enhances long-term prediction accuracy, but also ensures its reliability, demonstrating a significant advancement in trajectory forecasting.

Multi-Object Trajectory Prediction Based on Lane Information and Generative Adversarial Network

Nowadays, most trajectory prediction algorithms have difficulty simulating actual traffic behavior, and there is still a problem of large prediction errors. Therefore, this paper proposes a multi-object trajectory prediction algorithm based on lane information and foresight information. A Hybrid Dilated Convolution module based on the Channel Attention mechanism (CA-HDC) is developed to extract features, which improves the lane feature extraction in complicated environments and solves the problem of poor robustness of the traditional PINet. A lane information fusion module and a trajectory adjustment module based on the foresight information are developed. A socially acceptable trajectory with Generative Adversarial Networks (S-GAN) is developed to reduce the error of the trajectory prediction algorithm. The lane detection accuracy in special scenarios such as crowded, shadow, arrow, crossroad, and night are improved on the CULane dataset. The average F1-measure of the proposed lane detection has been increased by 4.1% compared to the original PINet. The trajectory prediction test based on D2-City indicates that the average displacement error of the proposed trajectory prediction algorithm is reduced by 4.27%, and the final displacement error is reduced by 7.53%. The proposed algorithm can achieve good results in lane detection and multi-object trajectory prediction tasks.

Nonparametric predictive model for sparse and irregular longitudinal data

We propose a kernel-based estimator to predict the mean response trajectory for sparse and irregularly measured longitudinal data. The kernel estimator is constructed by imposing weights based on the subject-wise similarity on L2 metric space between predictor trajectories, where we assume that an analogous fashion in predictor trajectories over time would result in a similar trend in the response trajectory among subjects. In order to deal with the curse of dimensionality caused by the multiple predictors, we propose an appealing multiplicative model with multivariate Gaussian kernels. This model is capable of achieving dimension reduction as well as selecting functional covariates with predictive significance. The asymptotic properties of the proposed nonparametric estimator are investigated under mild regularity conditions. We illustrate the robustness and flexibility of our proposed method via extensive simulation studies and an application to the Framingham Heart Study.

Query-Informed Multi-Agent Motion Prediction

In a dynamic environment, autonomous driving vehicles require accurate decision-making and trajectory planning. To achieve this, autonomous vehicles need to understand their surrounding environment and predict the behavior and future trajectories of other traffic participants. In recent years, vectorization methods have dominated the field of motion prediction due to their ability to capture complex interactions in traffic scenes. However, existing research using vectorization methods for scene encoding often overlooks important physical information about vehicles, such as speed and heading angle, relying solely on displacement to represent the physical attributes of agents. This approach is insufficient for accurate trajectory prediction models. Additionally, agents' future trajectories can be diverse, such as proceeding straight or making left or right turns at intersections. Therefore, the output of trajectory prediction models should be multimodal to account for these variations. Existing research has used multiple regression heads to output future trajectories and confidence, but the results have been suboptimal. To address these issues, we propose QINET, a method for accurate multimodal trajectory prediction for all agents in a scene. In the scene encoding part, we enhance the feature attributes of agent vehicles to better represent the physical information of agents in the scene. Our scene representation also possesses rotational and spatial invariance. In the decoder part, we use cross-attention and induce the generation of multimodal future trajectories by employing a self-learned query matrix. Experimental results demonstrate that QINET achieves state-of-the-art performance on the Argoverse motion prediction benchmark and is capable of fast multimodal trajectory prediction for multiple agents.

Vehicle Trajectory Prediction Method for Task Offloading in Vehicular Edge Computing

Real-time computation tasks in vehicular edge computing (VEC) provide convenience for vehicle users. However, the efficiency of task offloading seriously affects the quality of service (QoS). The predictive-mode task offloading is limited by computation resources, storage resources and the timeliness of vehicle trajectory data. Meanwhile, machine learning is difficult to deploy on edge servers. In this paper, we propose a vehicle trajectory prediction method based on the vehicle frequent pattern for task offloading in VEC. First, in the initialization stage, a T-pattern prediction tree (TPPT) is constructed based on the historical vehicle trajectory data. Secondly, when predicting the vehicle trajectory, the vehicle frequent itemset with the largest vehicle trajectory support is found in the vehicle frequent itemset of the TPPT. Finally, in the update stage, the TPPT is updated in real time with the predicted vehicle trajectory results. Meanwhile, based on the proposed prediction method, the strategies of task offloading and optimization algorithm are designed to minimize energy consumption with time constraints. The experiments are carried out on real-vehicle datasets and the Capital Bikeshare datasets. The results show that compared with the baseline T-pattern method, the accuracy of the prediction method is improved by more than 10% and the prediction efficiency is improved by more than 6.5 times. The vehicle trajectory prediction method based on the vehicle frequent pattern has high accuracy and prediction efficiency, which can solve the problem of vehicle trajectory prediction for task offloading.

Traffic Agents Trajectory Prediction Based on Spatial-Temporal Interaction Attention

Trajectory prediction aims to predict the movement intention of traffic participants in the future based on the historical observation trajectories. For traffic scenarios, pedestrians, vehicles and other traffic participants have social interaction of surrounding traffic participants in both time and spatial dimensions. Most previous studies only use pooling methods to simulate the interaction process between participants and cannot fully capture the spatio-temporal dependence, possibly accumulating errors with the increase in prediction time. To overcome these problems, we propose the Spatial-Temporal Interaction Attention-based Trajectory Prediction Network (STIA-TPNet), which can effectively model the spatial-temporal interaction information. Based on trajectory feature extraction, the novel Spatial-Temporal Interaction Attention Module (STIA Module) is proposed to extract the interaction relationships between traffic participants, including temporal interaction attention, spatial interaction attention, and spatio-temporal attention fusion. By adaptive allocation of attention weights, temporal interaction attention is a temporal attention mechanism used to capture the movement pattern of each traffic participant in the scene, which can learn the importance of historical trajectories at different moments to future behaviors. Since the participants number in recent traffic scenes dynamically changes, the spatial interaction attention is designed to abstract the traffic participants in the scene into graph nodes, and abstract the social interaction between participants into graph edges. Coupling the temporal and spatial interaction attentions can adaptively model the temporal-spatial information and achieve accurate trajectory prediction. By performing experiments on the INTERACTION dataset and the UTP (Unmanned Aerial Vehicle-based Trajectory Prediction) dataset, the experimental results show that the proposed method significantly improves the accuracy of trajectory prediction and outperforms the representative methods in comparison.

Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle-Pedestrian Interactions

Ensuring that intelligent vehicles do not cause fatal collisions remains a persistent challenge due to pedestrians' unpredictable movements and behavior. The potential for risky situations or collisions arising from even minor misunderstandings in vehicle-pedestrian interactions is a cause for great concern. Considerable research has been dedicated to the advancement of predictive models for pedestrian behavior through trajectory prediction, as well as the exploration of the intricate dynamics of vehicle-pedestrian interactions. However, it is important to note that these studies have certain limitations. In this paper, we propose a novel graph-based trajectory prediction model for vehicle-pedestrian interactions called Holistic Spatio-Temporal Graph Attention (HSTGA) to address these limitations. HSTGA first extracts vehicle-pedestrian interaction spatial features using a multi-layer perceptron (MLP) sub-network and max pooling. Then, the vehicle-pedestrian interaction features are aggregated with the spatial features of pedestrians and vehicles to be fed into the LSTM. The LSTM is modified to learn the vehicle-pedestrian interactions adaptively. Moreover, HSTGA models temporal interactions using an additional LSTM. Then, it models the spatial interactions among pedestrians and between pedestrians and vehicles using graph attention networks (GATs) to combine the hidden states of the LSTMs. We evaluate the performance of HSTGA on three different scenario datasets, including complex unsignalized roundabouts with no crosswalks and unsignalized intersections. The results show that HSTGA outperforms several state-of-the-art methods in predicting linear, curvilinear, and piece-wise linear trajectories of vehicles and pedestrians. Our approach provides a more comprehensive understanding of social interactions, enabling more accurate trajectory prediction for safe vehicle navigation.

A Novel Trajectory Feature-Boosting Network for Trajectory Prediction

Trajectory prediction is an essential task in many applications, including autonomous driving, robotics, and surveillance systems. In this paper, we propose a novel trajectory prediction network, called TFBNet (trajectory feature-boosting network), that utilizes trajectory feature boosting to enhance prediction accuracy. TFBNet operates by mapping the original trajectory data to a high-dimensional space, analyzing the change rules of the trajectory in this space, and finally aggregating the trajectory goals to generate the final trajectory. Our approach presents a new perspective on trajectory prediction. We evaluate TFBNet on five real-world datasets and compare it to state-of-the-art methods. Our results demonstrate that TFBNet achieves significant improvements in the ADE (average displacement error) and FDE (final displacement error) indicators, with increases of 46% and 52%, respectively. These results validate the effectiveness of our proposed approach and its potential to improve the performance of trajectory prediction models in various applications.

Vehicle Trajectory Prediction via Urban Network Modeling

Taxis are an important component of the transportation system, and empty taxis represent a significant waste of transportation resources. To alleviate the imbalance between supply and demand and relieve traffic congestion, real-time prediction of taxi trajectories is necessary. Most existing trajectory prediction studies focus on extracting time-series information but do not capture spatial information sufficiently. In this paper, we focus on the construction of an urban network and propose an urban topology-encoding spatiotemporal attention network (UTA) to address destination prediction problems. Firstly, this model discretizes the production and attraction units of transportation, combining them with key nodes in the road network to form an urban topological network. Secondly, GPS records are matched to the urban topological map to construct a topological trajectory, which significantly improves trajectory consistency and endpoint certainty, helping to model destination prediction problems. Thirdly, semantic information concerning surrounding space is attached to effectively mine the spatial dependencies of trajectories. Finally, after the topological encoding of city space and trajectories, this algorithm proposes a topological graph neural network to model the attention calculation with the trajectory context, comprehensively considering the spatiotemporal characteristics of the trajectories and improving prediction accuracy. We solve the prediction problems with the UTA model and also compare it with some other classical models, such as the HMM, RNN, LSTM, and transformer. The results suggest that all the models work well in combination with the proposed urban model (with a rough increase of 2%), while the UTA model is less affected by data sparsity.

Future Prediction of Shuttlecock Trajectory in Badminton Using Player's Information

Video analysis has become an essential aspect of net sports, such as badminton. Accurately predicting the future trajectory of balls and shuttlecocks can significantly benefit players by enhancing their performance and enabling them to devise effective game strategies. This paper aims to analyze data to provide players with an advantage in the fast-paced rallies of badminton matches. The paper delves into the innovative task of predicting future shuttlecock trajectories in badminton match videos and presents a method that takes into account both the shuttlecock position and the positions and postures of the players. In the experiments, players were extracted from the match video, their postures were analyzed, and a time-series model was trained. The results indicate that the proposed method improved accuracy by 13% compared to methods that solely used shuttlecock position information as input, and by 8.4% compared to methods that employed both shuttlecock and player position information as input.

A Parallelizable Task Offloading Model with Trajectory-Prediction for Mobile Edge Networks

As an emerging computing model, edge computing greatly expands the collaboration capabilities of the servers. It makes full use of the available resources around the users to quickly complete the task request coming from the terminal devices. Task offloading is a common solution for improving the efficiency of task execution on edge networks. However, the peculiarities of the edge networks, especially the random access of mobile devices, brings unpredictable challenges to the task offloading in a mobile edge network. In this paper, we propose a trajectory prediction model for moving targets in edge networks without users' historical paths which represents their habitual movement trajectory. We also put forward a mobility-aware parallelizable task offloading strategy based on a trajectory prediction model and parallel mechanisms of tasks. In our experiments, we compared the hit ratio of the prediction model, network bandwidth and task execution efficiency of the edge networks by using the EUA data set. Experimental results showed that our model is much better than random, non-position prediction parallel, non-parallel strategy-based position prediction. Where the task offloading hit rate is closed to the user's moving speed, when the speed is less 12.96 m/s, the hit rate can reach more than 80%. Meanwhile, we we also find that the bandwidth occupancy is significantly related to the degree of task parallelism and the number of services running on servers in the network. The parallel strategy can boost network bandwidth utilization by more than eight times when compared to a non-parallel policy as the number of parallel activities grows.

Modeling Trajectories Obtained from External Sensors for Location Prediction via NLP Approaches

Representation learning seeks to extract useful and low-dimensional attributes from complex and high-dimensional data. Natural language processing (NLP) was used to investigate the representation learning models to extract words' feature vectors using their sequential order in the text via word embeddings and language models that maintain their semantic meaning. Inspired by NLP, in this paper, we tackle the representation learning problem for trajectories, using NLP methods to encode external sensors positioned in the road network and generate the features' space to predict the next vehicle movement. We evaluate the vector representations of on-road sensors and trajectories using extrinsic and intrinsic strategies. Our results have shown the potential of natural language models to describe the space of features on trajectory applications as the next location prediction.

Decoding trajectories of imagined hand movement using electrocorticograms for brain-machine interface

OBJECTIVE

Reaching hand movement is an important motor skill actively examined in brain-computer interface (BCI). Among various components of movement analyzed is the hand's trajectory, which describes the hand's continuous positions in three-dimensional space. While a large body of studies have investigated the decoding of real movements and the reconstruction of real hand movement trajectories from neural signals, fewer studies have attempted to decode the trajectory of imagined hand movement. To develop BCI systems for patients with hand motor dysfunctions, the systems essentially require to achieve movement-free control of external devices, which is only possible through successful decoding of purely imagined hand movement.

APPROACH

To achieve this goal, this study used a machine learning technique (i.e., the variational Bayesian least square) to analyze the electrocorticogram (ECoG) of eighteen epilepsy patients obtained from when they performed movement execution (ME) and kinesthetic movement imagination (KMI) of the reach-and-grasp hand action.

MAIN RESULTS

The variational Bayesian decoding model was able to successfully predict the imagined trajectories of hand movement significantly above chance level. The Pearson's correlation coefficient between imagined and predicted trajectories was 0.3393 and 0.4936 for the KMI (KMI trials only) and MEKMI paradigm (alternating trials of ME and KMI) respectively.

SIGNIFICANCE

This study demonstrated a high accuracy of prediction for trajectories of imagined hand movement, and more importantly, higher decoding accuracy of imagined trajectories in the MEKMI paradigm than in the KMI paradigm solely.

Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach

Currently, the importance of autonomous operating devices is rising with the increasing number of applications that run on robotic platforms or self-driving cars. The context of social robotics assumes that robotic platforms operate autonomously in environments where people perform their daily activities. The ability to re-identify the same people through a sequence of images is a critical component for meaningful human-robot interactions. Considering the quick reactions required by a self-driving car for safety considerations, accurate real-time tracking and people trajectory prediction are mandatory. In this paper, we introduce a real-time people re-identification system based on a trajectory prediction method. We tackled the problem of trajectory prediction by introducing a system that combines semantic information from the environment with social influence from the other participants in the scene in order to predict the motion of each individual. We evaluated the system considering two possible case studies, social robotics and autonomous driving. In the context of social robotics, we integrated the proposed re-identification system as a module into the AMIRO framework that is designed for social robotic applications and assistive care scenarios. We performed multiple experiments in order to evaluate the performance of our proposed method, considering both the trajectory prediction component and the person re-identification system. We assessed the behaviour of our method on existing datasets and on real-time acquired data to obtain a quantitative evaluation of the system and a qualitative analysis. We report an improvement of over 5% for the MOTA metric when comparing our re-identification system with the existing module, on both evaluation scenarios, social robotics and autonomous driving.

A real-time data assimilative forecasting system for animal tracking

Monitoring technologies now provide real‐time animal location information, which opens up the possibility of developing forecasting systems to fuse these data with movement models to predict future trajectories. State‐space modeling approaches are well established for retrospective location estimation and behavioral inference through state and parameter estimation. Here we use a state‐space model within a comprehensive data assimilative framework for probabilistic animal movement forecasting. Real‐time location information is combined with stochastic movement model predictions to provide forecasts of future animal locations and trajectories, as well as estimates of key behavioral parameters. Implementation uses ensemble‐based sequential Monte Carlo methods (a particle filter). We first apply the framework to an idealized case using a nondimensional animal movement model based on a continuous‐time random walk process. A set of numerical forecasting experiments demonstrates the workflow and key features, such as the online estimation of behavioral parameters using state augmentation, the use of potential functions for habitat preference, and the role of observation error and sampling frequency on forecast skill. For a realistic demonstration, we adapt the framework to short‐term forecasting of the endangered southern resident killer whale (SRKW) in the Salish Sea using visual sighting information wherein the potential function reflects historical habitat utilization of SRKW. We successfully estimate whale locations up to 2.5 h in advance with a moderate prediction error (<5 km), providing reasonable lead‐in time to mitigate vessel–whale interactions. It is argued that this forecasting framework can be used to synthesize diverse data types and improve animal movement models and behavioral understanding and has the potential to lead to important advances in movement ecology.

Probabilistic Maritime Trajectory Prediction in Complex Scenarios Using Deep Learning

Maritime activity is expected to increase, and therefore also the need for maritime surveillance and safety. Most ships are obligated to identify themselves with a transponder system like the Automatic Identification System (AIS) and ships that do not, intentionally or unintentionally, are referred to as dark ships and must be observed by other means. Knowing the future location of ships can not only help with ship/ship collision avoidance, but also with determining the identity of these dark ships found in, e.g., satellite images. However, predicting the future location of ships is inherently probabilistic and the variety of possible routes is almost limitless. We therefore introduce a Bidirectional Long-Short-Term-Memory Mixture Density Network (BLSTM-MDN) deep learning model capable of characterising the underlying distribution of ship trajectories. It is consequently possible to predict a probabilistic future location as opposed to a deterministic location. AIS data from 3631 different cargo ships are acquired from a region west of Norway spanning 320,000 sqkm. Our implemented BLSTM-MDN model characterizes the conditional probability of the target, conditioned on an input trajectory using an 11-dimensional Gaussian distribution and by inferring a single target from the distribution, we can predict several probable trajectories from the same input trajectory with a test Negative Log Likelihood loss of −9.96 corresponding to a mean distance error of 2.53 km 50 min into the future. We compare our model to both a standard BLSTM and a state-of-the-art multi-headed self-attention BLSTM model and the BLSTM-MDN performs similarly to the two deterministic deep learning models on straight trajectories, but produced better results in complex scenarios.

Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization

It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories in complex traffic situations. However, predicting the future behavior of vehicles is a challenging issue because traffic vehicles each have different drivers with different driving tendencies and intentions and they interact with each other. This paper presents a Long Short-Term Memory (LSTM) encoder–decoder model that utilizes an attention mechanism that focuses on certain information to predict vehicles’ trajectories. The proposed model was trained using the Highway Drone (HighD) dataset, which is a high-precision, large-scale traffic dataset. We also compared this model to previous studies. Our model effectively predicted future trajectories by using an attention mechanism to manage the importance of the driving flow of the target and adjacent vehicles and the target vehicle’s dynamics in each driving situation. Furthermore, this study presents a method of linearizing the road geometry such that the trajectory prediction model can be used in a variety of road environments. We verified that the road geometry linearization mechanism can improve the trajectory prediction model’s performance on various road environments in a virtual test-driving simulator constructed based on actual road data.

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.

Complex Environment Path Planning for Unmanned Aerial Vehicles

Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT*. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments.

Individualized Prognostic Prediction of the Long-Term Functional Trajectory in Pediatric Acquired Brain Injury

In pediatric acquired brain injury, heterogeneity of functional response to specific rehabilitation treatments is a key confound to medical decisions and outcome prediction. We aimed to identify patient subgroups sharing comparable trajectories, and to implement a method for the early prediction of the long-term recovery course from clinical condition at first discharge. 600 consecutive patients with acquired brain injury (7.4 years ± 5.2; 367 males; median GCS = 6) entered a standardized rehabilitation program. Functional Independent Measure scores were measured yearly, until year 7. We classified the functional trajectories in clusters, through a latent class model. We performed single-subject prediction of trajectory membership in cases unseen during model fitting. Four trajectory types were identified (post.prob. > 0.95): high-start fast (N = 92), low-start fast (N = 168), slow (N = 130) and non-responders (N = 210). Fast responders were older (chigh = 1.8; clow = 1.1) than non-responders and suffered shorter coma (chigh = -14.7; clow = -4.3). High-start fast-responders had shorter length of stay (c = -1.6), and slow responders had lower incidence of epilepsy (c = -1.4), than non-responders (p < 0.001). Single-subject trajectory could be predicted with high accuracy at first discharge (accuracy = 0.80). In conclusion, we stratified patients based on the evolution of their response to a specific treatment program. Data at first discharge predicted the response over 7 years. This method enables early detection of the slow responders, who show poor post-acute functional gains, but achieve recovery comparable to fast responders by year 7. Further external validation in other rehabilitation programs is warranted.

Rating Player Actions in Soccer

We present a data-driven model that rates actions of the player in soccer with respect to their contribution to ball possession phases. This study approach consists of two interconnected parts: (i) a trajectory prediction model that is learned from real tracking data and predicts movements of players and (ii) a prediction model for the outcome of a ball possession phase. Interactions between players and a ball are captured by a graph recurrent neural network (GRNN) and we show empirically that the network reliably predicts both, player trajectories as well as outcomes of ball possession phases. We derive a set of aggregated performance indicators to compare players with respect to. to their contribution to the success of their team.

Mobility-Aware Privacy-Preserving Mobile Crowdsourcing

The increasing popularity of smartphones and location-based service (LBS) has brought us a new experience of mobile crowdsourcing marked by the characteristics of network-interconnection and information-sharing. However, these mobile crowdsourcing applications suffer from various inferential attacks based on mobile behavioral factors, such as location semantic, spatiotemporal correlation, etc. Unfortunately, most of the existing techniques protect the participant’s location-privacy according to actual trajectories. Once the protection fails, data leakage will directly threaten the participant’s location-related private information. It open the issue of participating in mobile crowdsourcing service without actual locations. In this paper, we propose a mobility-aware trajectory-prediction solution, TMarkov, for achieving privacy-preserving mobile crowdsourcing. Specifically, we introduce a time-partitioning concept into the Markov model to overcome its traditional limitations. A new transfer model is constructed to record the mobile user’s time-varying behavioral patterns. Then, an unbiased estimation is conducted according to Gibbs Sampling method, because of the data incompleteness. Finally, we have the TMarkov model which characterizes the participant’s dynamic mobile behaviors. With TMarkov in place, a mobility-aware spatiotemporal trajectory is predicted for the mobile user to participate in the crowdsourcing application. Extensive experiments with real-world dataset demonstrate that TMarkov well balances the trade-off between privacy preservation and data usability.

Driving Environment Perception Based on the Fusion of Vehicular Wireless Communications and Automotive Remote Sensors

Driving environment perception for automated vehicles is typically achieved by the use of automotive remote sensors such as radars and cameras. A vehicular wireless communication system can be viewed as a new type of remote sensor that plays a central role in connected and automated vehicles (CAVs), which are capable of sharing information with each other and also with the surrounding infrastructure. In this paper, we present the design and implementation of driving environment perception based on the fusion of vehicular wireless communications and automotive remote sensors. A track-to-track fusion of high-level sensor data and vehicular wireless communication data was performed to accurately and reliably locate the remote target in the vehicle surroundings and predict the future trajectory. The proposed approach was implemented and evaluated in vehicle tests conducted at a proving ground. The experimental results demonstrate that using vehicular wireless communications in conjunction with the on-board sensors enables improved perception of the surrounding vehicle located at varying longitudinal and lateral distances. The results also indicate that vehicle future trajectory and potential crash involvement can be reliably predicted with the proposed system in different cut-in driving scenarios.

Driven by Vision: Learning Navigation by Visual Localization and Trajectory Prediction

When driving, people make decisions based on current traffic as well as their desired route. They have a mental map of known routes and are often able to navigate without needing directions. Current published self-driving models improve their performances when using additional GPS information. Here we aim to push forward self-driving research and perform route planning even in the complete absence of GPS at inference time. Our system learns to predict in real-time vehicle’s current location and future trajectory, on a known map, given only the raw video stream and the final destination. Trajectories consist of instant steering commands that depend on present traffic, as well as longer-term navigation decisions towards a specific destination. Along with our novel proposed approach to localization and navigation from visual data, we also introduce a novel large dataset in an urban environment, which consists of video and GPS streams collected with a smartphone while driving. The GPS is automatically processed to obtain supervision labels and to create an analytical representation of the traversed map. In tests, our solution outperforms published state of the art methods on visual localization and steering and provides reliable navigation assistance between any two known locations. We also show that our system can adapt to short and long-term changes in weather conditions or the structure of the urban environment. We make the entire dataset and the code publicly available.

A Real-Time Trajectory Prediction Method of Small-Scale Quadrotors Based on GPS Data and Neural Network

This paper proposes a real-time trajectory prediction method for quadrotors based on a bidirectional gated recurrent unit model. Historical trajectory data of ten types of quadrotors were obtained. The bidirectional gated recurrent units were constructed and utilized to learn the historic data. The prediction results were compared with the traditional gated recurrent unit method to test its prediction performance. The efficiency of the proposed algorithm was investigated by comparing the training loss and training time. The results over the testing datasets showed that the proposed model produced better prediction results than the baseline models for all scenarios of the testing datasets. It was also found that the proposed model can converge to a stable state faster than the traditional gated recurrent unit model. Moreover, various types of training samples were applied and compared. With the same randomly selected test datasets, the performance of the prediction model can be improved by selecting the historical trajectory samples of the quadrotors close to the weight or volume of the target quadrotor for training. In addition, the performance of stable trajectory samples is significantly better than that with unstable trajectory segments with a frequent change of speed and direction with large angles.

A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network

Ship trajectory prediction is a key requisite for maritime navigation early warning and safety, but accuracy and computation efficiency are major issues still to be resolved. The research presented in this paper introduces a deep learning framework and a Gate Recurrent Unit (GRU) model to predict vessel trajectories. First, series of trajectories are extracted from Automatic Identification System (AIS) ship data (i.e., longitude, latitude, speed, and course). Secondly, main trajectories are derived by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Next, a trajectory information correction algorithm is applied based on a symmetric segmented-path distance to eliminate the influence of a large number of redundant data and to optimize incoming trajectories. A recurrent neural network is applied to predict real-time ship trajectories and is successively trained. Ground truth data from AIS raw data in the port of Zhangzhou, China were used to train and verify the validity of the proposed model. Further comparison was made with the Long Short-Term Memory (LSTM) network. The experiments showed that the ship’s trajectory prediction method can improve computational time efficiency even though the prediction accuracy is similar to that of LSTM.

Road-Aware Trajectory Prediction for Autonomous Driving on Highways

For driving safely and comfortably, the long-term trajectory prediction of surrounding vehicles is essential for autonomous vehicles. For handling the uncertain nature of trajectory prediction, deep-learning-based approaches have been proposed previously. An on-road vehicle must obey road geometry, i.e., it should run within the constraint of the road shape. Herein, we present a novel road-aware trajectory prediction method which leverages the use of high-definition maps with a deep learning network. We developed a data-efficient learning framework for the trajectory prediction network in the curvilinear coordinate system of the road and a lane assignment for the surrounding vehicles. Then, we proposed a novel output-constrained sequence-to-sequence trajectory prediction network to incorporate the structural constraints of the road. Our method uses these structural constraints as prior knowledge for the prediction network. It is not only used as an input to the trajectory prediction network, but is also included in the constrained loss function of the maneuver recognition network. Accordingly, the proposed method can predict a feasible and realistic intention of the driver and trajectory. Our method has been evaluated using a real traffic dataset, and the results thus obtained show that it is data-efficient and can predict reasonable trajectories at merging sections.

Vehicle Trajectory Prediction and Collision Warning via Fusion of Multisensors and Wireless Vehicular Communications

Driver inattention is one of the leading causes of traffic crashes worldwide. Providing the driver with an early warning prior to a potential collision can significantly reduce the fatalities and level of injuries associated with vehicle collisions. In order to monitor the vehicle surroundings and predict collisions, on-board sensors such as radar, lidar, and cameras are often used. However, the driving environment perception based on these sensors can be adversely affected by a number of factors such as weather and solar irradiance. In addition, potential dangers cannot be detected if the target is located outside the limited field-of-view of the sensors, or if the line of sight to the target is occluded. In this paper, we propose an approach for designing a vehicle collision warning system based on fusion of multisensors and wireless vehicular communications. A high-level fusion of radar, lidar, camera, and wireless vehicular communication data was performed to predict the trajectories of remote targets and generate an appropriate warning to the driver prior to a possible collision. We implemented and evaluated the proposed vehicle collision system in virtual driving environments, which consisted of a vehicle–vehicle collision scenario and a vehicle–pedestrian collision scenario.

Pedestrian Trajectory Prediction in Extremely Crowded Scenarios

Pedestrian trajectory prediction under crowded circumstances is a challenging problem owing to human interaction and the complexity of the trajectory pattern. Various methods have been proposed for solving this problem, ranging from traditional Bayesian analysis to Social Force model and deep learning methods. However, most existing models heavily depend on specific scenarios because the trajectory model is constructed in absolute coordinates even though the motion trajectory as well as human interaction are in relative motion. In this study, a novel trajectory prediction model is proposed to capture the relative motion of pedestrians in extremely crowded scenarios. Trajectory sequences and human interaction are first represented with relative motion and then integrated to our model to predict pedestrians' trajectories. The proposed model is based on Long Short Term Memory (LSTM) structure and consists of an encoder and a decoder which are trained by truncated back propagation. In addition, an anisotropic neighborhood setting is proposed instead of traditional neighborhood analysis. The proposed approach is validated using trajectory data acquired at an extremely crowded train station in Tokyo, Japan. The trajectory prediction experiments demonstrated that the proposed method outperforms existing methods and is stable for predictions of varying length even when the model is trained with a controlled short trajectory sequence.