Self-driving vehicles improve safety by predicting surrounding vehicles' trajectories.
problem Ensuring safety of self-driving vehicles through better trajectory prediction.
method Developed a Convolutional Neural Network to forecast vehicle trajectories from raw data.
result Improvement over baseline models in trajectory forecasting accuracy.
Paper predicts future vehicle trajectories for safer AVs.
problem Improving accuracy of long-term vehicle trajectory prediction for autonomous vehicles.
method Dual LSTM network for automatic learning of driver behaviors and future trajectory prediction.
result The method achieves lower RMSE for longitudinal and lateral predictions compared to state-of-the-art methods.
New method predicts vehicle trajectories using map lane centers.
problem Accurate long-term vehicle trajectory prediction.
method Uses map lane centers to generate goal paths and predict trajectories.
result Model outperforms state-of-the-art approaches for 6-second horizon predictions.
This work improves vehicle trajectory prediction for safer self-driving cars.
problem Improving trajectory predictions for safer self-driving cars.
method Deep-learning-based method combining AI and physically grounded models.
result Proposes a method that outperforms existing state-of-the-art.
The paper predicts human-like driving behavior of other vehicles for safer AVs.
problem Safe and efficient interaction of AVs with other vehicles.
method Hierarchical inverse reinforcement learning considering both discrete and continuous decisions.
result The proposed approach accurately predicts both discrete and continuous driving behaviors.
Generative model learns vehicle trajectory distributions for better data generalization.
problem Data sparsity and privacy issues in urban vehicle trajectory analysis.
method Generative adversarial imitation learning framework for urban vehicle trajectory generation.
result TrajGAIL model produces synthetic trajectories similar to real ones, achieving significant performance gains.
Study uses vehicle trajectory data to predict traffic incidents on highways.
problem Early detection of traffic incidents to reduce secondary crashes.
method Machine learning algorithms (Logistic Regression, Random Forest, Extreme Gradient Boost, Artificial Neural Network) applied to vehicle trajectory data.
result Random Forest model performs best for incident prediction.
Predicts multiple vehicle trajectories efficiently.
problem Predicting uncertain future motions of agents in dynamic scenes.
method Probabilistic framework learning latent variables for multi-step future modeling.
result State-of-the-art predictions on vehicle trajectory datasets.
Adaptive vehicle trajectory prediction for safer autonomous driving.
problem Inability of current methods to guarantee physical feasibility and adapt to human driving policies.
method Bayesian recurrent neural network combining policy and physical models, with gradient-based training and parameter adaptation.
result The proposed method ensures physical feasibility and adaptability to human driving policies.
Develops a neural network for precise vehicle trajectory prediction.
problem Improving situational awareness in vehicular networks for safety applications.
method Two-layer neural network predicting vehicle parameters and trajectory points.
result Significantly improved prediction accuracy compared to existing methods.
This paper compares machine learning methods for recognizing lane change intentions from vehicle trajectories.
problem Accurately detecting and predicting lane change processes in autonomous vehicles.
method Comparison of different machine learning methods on high-dimensional time series data.
result Ensemble methods reduce Type II and Type III classification errors, while LightGBM outperforms XGBoost in training efficiency.
This research predicts vehicle movements by analyzing their intentions relative to road lanes.
problem Accurately forecasting vehicles' future movements for safe autonomous driving.
method LSTM networks with attention mechanisms applied to spatio-temporal graphs of road lanes.
result The model outperforms other state-of-the-art models in several metrics.
Graph neural network predicts vehicle interactions and trajectories for autonomous driving.
problem Predicting future motion of vehicles in traffic scenes.
method Graph neural network that jointly predicts interaction modes and 5-second future trajectories.
result Jointly predicting trajectories and interaction modes leads to lower trajectory error.
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
problem Lack of infrastructure and pooled vehicle info in existing AV datasets.
method Developed VTrackIt, a synthetic dataset with intelligent infrastructure and pooled vehicle info, and introduced InfraGAN for trajectory predictions.
result VTrackIt reduces high-risk edge cases in AV trajectory predictions.
Proposes a generic prediction architecture for autonomous vehicles considering both rational and irrational driving behaviors.
problem Accurately predicting future behaviors of surrounding vehicles for safe autonomous vehicle planning.
method Combines learning-based and planning-based models to address rationalities in human behavior.
result Stable prediction performance under various unseen driving scenarios.
System predicts vehicle interactions and trajectories with uncertainty.
problem Predicting future vehicle trajectories with uncertainty.
method Hierarchical Bayesian Generative Modeling with categorized and real-valued coordination variables.
result Categorized coordination better captures multi-modality and generates more diverse samples.
PRECOG predicts future interactions between AVs and other drivers.
problem Autonomous vehicles need to predict human drivers' intentions for safe road behavior.
method Probabilistic forecasting model trained on real and simulated data.
result Our model predicts future interactions more accurately than existing methods.
Paper proposes a statistical approach for predicting lane changes in highway scenarios.
problem Early recognition of lane changes for automated driving.
method Prototype trajectories generated from real data using Agglomerative Hierarchical Clustering. Maneuver prediction via Boosted Decision Trees and mixture model.
result Improved performance for lane change and trajectory prediction compared to a reference approach.
A framework clusters vehicle motion trajectories efficiently.
problem Costly manual annotation of vehicle motion data.
method Five-stage framework: align, embed, extract, embed, cluster.
result Framework achieves promising results on real-world dataset.
Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario. However, distinct driving environments usually contain various possible driving…
CitySim dataset captures vehicle trajectories for safety research.
problem Lack of fine-grain vehicle trajectories for safety-oriented research.
method Five-step procedure: video stabilization, object filtering, stitching, detection, and error filtering.
result CitySim dataset improves safety evaluations and facilitates digital-twin research.
Predicts destinations and routes from partial trajectory data.
problem Predicting destinations and routes from partial trajectory data for applications like parking suggestions and ride-sharing.
method Three-step procedure: k-d tree-based space discretization, recurrent neural network for destination prediction, and route calculation.
result Best models predict destinations with a mean error of 1.3 km and 1.43 km.
Predict real-time crash risks during hurricane evacuations using connected vehicle data.
problem Mitigate crash risks during hurricane evacuations by predicting high-risk locations.
method Used connected vehicle data to predict crash risks in real-time, considering weather and traffic features.
result Gaussian Process Boosting and Extreme Gradient Boosting models performed best, with recall of 0.91.
Improved motion prediction for self-driving cars using trajectory sets and auxiliary losses.
problem Accurately predicting future vehicle motion for self-driving cars.
method Classification over trajectory sets with an auxiliary loss for off-road predictions and spatial-temporal relationships.
result Significant improvement in motion prediction performance on small datasets using map information.
In this paper we propose a new method to predict the final destination of vehicle trips based on their initial partial trajectories. We first review how we obtained clustering of trajectories that describes user behaviour. Then, we explain how we model main traffic flow patterns by a mixture of 2d Gaussian distribution…
We apply variational inference to learn vehicle trajectory parameters from noisy data.
problem Learning parameters for vehicle trajectory estimation from noisy measurements.
method Gaussian variational inference with parameter learning in a motion and sensor model context.
result High-quality state estimates achieved even with outliers and false loop closures.
A new clustering algorithm for functional data using binary trees.
problem Clustering multivariate functional data with measurement errors.
method Recursive binary tree splitting for data clustering.
result Good performance in various complex settings, including vehicle trajectories.
Improved traffic flow prediction model using Kalman filter noise reduction.
problem Low accuracy in predicting traffic flow parameters due to limited connected vehicle data.
method Combined LSTM with Kalman filter-based RTS noise reduction.
result Reduced prediction errors by 50-70% for speed and space headway.
Deep CNNs predict multiple actor trajectories for safer autonomous driving.
problem Uncertainty and variety of traffic behaviors in autonomous driving.
method Convert actor surroundings into images, feed into deep CNNs.
result Successfully tested on SDVs in closed-course tests.
CGNS predicts probabilistic trajectories for safer autonomous systems.
problem Accurate probabilistic trajectory prediction for dynamic obstacles in complex scenarios.
method CGNS combines latent space learning and variational divergence minimization, incorporating static and interaction information with soft attention mechanisms and regularization for soft constraints.
result CGNS outperforms baseline approaches in pedestrian trajectory prediction and naturalistic driving datasets.
MPE models traffic trajectory data to predict next locations.
problem Predicting next locations from traffic trajectory data.
method Mobility pattern embedding model MPE.
result MPE significantly outperforms state-of-the-art methods in next location prediction.
Deep neural network reconstructs traffic speeds from sparse vehicle data.
problem Reconstructing traffic speeds from limited probe vehicle data.
method Convolutional neural network architecture for spatio-temporal learning.
result The method can reconstruct traffic speeds with low probe vehicle penetration.
The paper proposes a model to forecast traffic motion from sensor data.
problem Accurately predicting traffic motion for safe vehicle maneuvers.
method Implicit latent variable model using interaction graphs and graph neural networks.
result Achieves state-of-the-art motion forecasting and interaction understanding.
A drone-based MOT algorithm tracks vehicles using neural network detections and TPMBM filter.
problem Tracking multiple vehicles from drone-mounted cameras.
method Neural network for object detection, TPMBM filter for trajectory estimation, von-Mises Fisher distribution for DOA.
result TPMBM filter optimally estimates vehicle trajectories.
This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but new, environments. Previously, Chen et. al. combined Markovian-based and clustering-based approaches to learn motion primitives in a grid-bas…
Study uses trajectory embedding to measure place function similarity at fine spatial granularity.
problem Measuring place function similarity at fine spatial granularity.
method Trajectory embedding to reduce dimensions and measure similarity of place functions.
result Embedding similarity can be a metric proxy for place functions at fine spatial granularity.
Enhanced framework predicts transportation modes from GPS data.
problem Predicting transportation modes from GPS records.
method Extended Etemad et al. framework, investigated wrapper search and information retrieval methods for feature selection, compared with deep learning methods.
result Framework achieved better performance than related papers.
Fast risk assessment for autonomous vehicles using learned agent futures.
problem Risk assessment for autonomous vehicles given probabilistic predictions of other agents' futures.
method Non-sampling based methods using deep neural networks for probabilistic predictions, with Gaussian and non-Gaussian mixture models for agent positions and controls.
result Effective risk assessment for low probability events using learned models of agent futures.
This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories. The dynamics are represented by a sequence of factor graphs, which enable learning of traffic dynamics from limited Lagrangian measurements using an effic…
Deep learning tool classifies urban delivery vehicles.
problem Counting and categorizing delivery vehicles in cities.
method Developed annotated database and retrained CNNs.
result Accurate classification of 90%+ for 3 vehicle classes.
Deep learning models control vehicle dynamics on a track.
problem Coupled longitudinal and lateral control of a vehicle.
method Trained two neural networks (MLP and CNN) to compute controls based on high-fidelity simulations.
result Deep learning models outperform conventional controllers on a challenging track.
HighD dataset captures naturalistic vehicle behavior on German highways for automated driving validation.
problem Current measurement methods fail to meet requirements for scenario-based validation of highly automated vehicles.
method Aerial perspective data collection fulfilling naturalistic behavior, quantity, and variety requirements.
result 16.5 hours of measurements from six locations, 110,000 vehicles, 45,000 km driven, 5600 lane changes.
Paper automates car negotiation in intersections using Q-learning.
problem Automated vehicles negotiate with human-driven cars in intersections.
method Deep Q-learning applied to simulated traffic with various driver behaviors.
result 98% success rate in avoiding collisions with other vehicles.
This paper surveys DRL for autonomous vehicle motion planning.
problem Designing intelligent motion planning for autonomous vehicles.
method Deep Reinforcement Learning (DRL) for hierarchical motion planning.
result Survey of state-of-the-art DRL solutions for autonomous vehicle motion planning.
Develops a new model for controllable and realistic traffic simulation.
problem Lack of models that offer both controllability and realism in traffic simulation.
method Guided Conditional Diffusion (CTG) model using diffusion modeling and differentiable logic.
result Improves controllability-realism tradeoff over strong baselines.
Generative model predicts vehicle faults up to 1000 hours in advance.
problem Forecasting vehicle faults for predictive maintenance.
method Generative model trained on US Army data, incorporating real-world factors.
result Highly accurate predictions of time to first fault.
Proposes CTSDG model for better vehicle intention prediction across domains.
problem Domain generalization for vehicle intention prediction in dynamic environments.
method Structural causal model with recurrent latent variable integration.
result Consistent improvement in prediction accuracy compared to state-of-the-art methods.
This paper improves traffic actor movement predictions using GANs and a differentiable rasterizer.
problem Predicting future movements of traffic actors for safe autonomous driving.
method A novel raster-based conditional GAN architecture with a differentiable rasterizer.
result The proposed method outperforms state-of-the-art GAN-based baselines.