Search Results for author:
Found 31 papers, 6 papers with code
Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks
This paper presents an online smooth-path lane-change control framework.
Object-centric Video Representation for Long-term Action Anticipation
To recognize and predict human-object interactions, we use a Transformer-based neural architecture which allows the "retrieval" of relevant objects for action anticipation at various time scales.
Ranked #2 on
Long Term Action Anticipation
on Ego4D
Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning
The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) may largely depend on their acceptance by society, for which their perceived trustworthiness and interpretability to riders are crucial.
Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction
As far as we know, this is the first work to address the lack of benchmarks and techniques for trajectory imputation and prediction in a unified manner.
AdamsFormer for Spatial Action Localization in the Future
SALF is challenging because it requires understanding the underlying physics of video observations to predict future action locations accurately.
Latency Matters: Real-Time Action Forecasting Transformer
We present RAFTformer, a real-time action forecasting transformer for latency aware real-world action forecasting applications.
DRAMA: Joint Risk Localization and Captioning in Driving
We make this data available to the community for further research.
DIDER: Discovering Interpretable Dynamically Evolving Relations
DIDER discovers an interpretable sequence of inter-agent interactions by disentangling the task of latent interaction prediction into sub-interaction prediction and duration estimation.
Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction
To address these issues, we propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels.
Weakly-Supervised Online Action Segmentation in Multi-View Instructional Videos
Experimental results show efficacy of the proposed methods both qualitatively and quantitatively in two domains of cooking and assembly.
Important Object Identification with Semi-Supervised Learning for Autonomous Driving
We propose a novel approach for important object identification in egocentric driving scenarios with relational reasoning on the objects in the scene.
Multi-Objective Diverse Human Motion Prediction With Knowledge Distillation
Obtaining accurate and diverse human motion prediction is essential to many industrial applications, especially robotics and autonomous driving.
LOKI: Long Term and Key Intentions for Trajectory Prediction
We also propose a model that jointly performs trajectory and intention prediction, showing that recurrently reasoning about intention can assist with trajectory prediction.
RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting
Motion forecasting plays a significant role in various domains (e. g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of historical observations.
Shared Cross-Modal Trajectory Prediction for Autonomous Driving
At test time, a single input modality (e. g., LiDAR data) is required to generate predictions from the input perspective (i. e., in the LiDAR space), while taking advantages from the model trained with multiple sensor modalities.
Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast
This paper considers the problem of multi-modal future trajectory forecast with ranking.
SSP: Single Shot Future Trajectory Prediction
Third, the semantic context of the scene are modeled and take into account the environmental constraints that potentially influence the future motion.
Shared Cross-Modal Trajectory Prediction for Autonomous Driving
At test time, a single input modality (e. g., LiDAR data) is required to generate predictions from the input perspective (i. e., in the LiDAR space), while taking advantages from the model trained with multiple sensor modalities.
TITAN: Future Forecast using Action Priors
In an attempt to address this problem, we introduce TITAN (Trajectory Inference using Targeted Action priors Network), a new model that incorporates prior positions, actions, and context to forecast future trajectory of agents and future ego-motion.
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
In this paper, we propose a generic trajectory forecasting framework (named EvolveGraph) with explicit relational structure recognition and prediction via latent interaction graphs among multiple heterogeneous, interactive agents.
Ranked #12 on
Trajectory Prediction
on Stanford Drone
Potential Field: Interpretable and Unified Representation for Trajectory Prediction
From the generated potential fields, we further estimate future motion direction and speed, which are modeled as Gaussian distributions to account for the multi-modal nature of the problem.
NEMO: Future Object Localization Using Noisy Ego Priors
In the proposed approach, a predictive distribution of future forecast is jointly modeled with the uncertainty of predictions.
Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers.
DROGON: A Trajectory Prediction Model based on Intention-Conditioned Behavior Reasoning
We propose a Deep RObust Goal-Oriented trajectory prediction Network (DROGON) for accurate vehicle trajectory prediction by considering behavioral intentions of vehicles in traffic scenes.
Looking to Relations for Future Trajectory Forecast
Inferring relational behavior between road users as well as road users and their surrounding physical space is an important step toward effective modeling and prediction of navigation strategies adopted by participants in road scenes.
Deep Learning 3D Shapes Using Alt-az Anisotropic 2-Sphere Convolution
The ground-breaking performance obtained by deep convolutional neural networks (CNNs) for image processing tasks is inspiring research efforts attempting to extend it for 3D geometric tasks.
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving.
Learning Hand Articulations by Hallucinating Heat Distribution
As an additional modality to depth data, we present a function of geometric properties on the surface of the hand described by heat diffusion.
Robust Hand Pose Estimation During the Interaction With an Unknown Object
Our main insight is that the shape of an object causes a configuration of the hand in the form of a hand grasp.
DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed With Deep Features
Our matrix completion algorithm uses these 'spatio-temporal' activation features and the corresponding known pose parameter values to to estimate the unknown pose parameters of the input feature vector.
A Collaborative Filtering Approach to Real-Time Hand Pose Estimation
Specifically, we recast the hand pose estimation problem as the cold-start problem for a new user with unknown item ratings in a recommender system.
