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Found 28 papers, 4 papers with code
TrACT: A Training Dynamics Aware Contrastive Learning Framework for Long-tail Trajectory Prediction
In this paper, we propose to incorporate richer training dynamics information into a prototypical contrastive learning framework.
AMEND: A Mixture of Experts Framework for Long-tailed Trajectory Prediction
Such a long-tail effect causes prediction models to underperform on the tail portion of the data distribution containing safety-critical scenarios.
Ranked #1 on
Trajectory Prediction
on ETH/UCY
DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction
Road user trajectory prediction in dynamic environments is a challenging but crucial task for various applications, such as autonomous driving.
A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction
To this end, we propose a novel egocentric trajectory prediction model that benefits from multimodal sources of data fused in an effective and efficient step-wise hierarchical fashion and two auxiliary tasks designed to learn more robust representation of scene dynamics.
CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving
Benchmarking is a common method for evaluating trajectory prediction models for autonomous driving.
DESTINE: Dynamic Goal Queries with Temporal Transductive Alignment for Trajectory Prediction
Predicting temporally consistent road users' trajectories in a multi-agent setting is a challenging task due to unknown characteristics of agents and their varying intentions.
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving
In autonomous driving, behavior prediction is fundamental for safe motion planning, hence the security and robustness of prediction models against adversarial attacks are of paramount importance.
Intend-Wait-Perceive-Cross: Exploring the Effects of Perceptual Limitations on Pedestrian Decision-Making
Current research on pedestrian behavior understanding focuses on the dynamics of pedestrians and makes strong assumptions about their perceptual abilities.
Learn TAROT with MENTOR: A Meta-Learned Self-Supervised Approach for Trajectory Prediction
More specifically, 1) we define TAROT prediction as a novel self-supervised proxy task to identify the complex heterogeneous structure of the map.
NeurIPS 2022 Competition: Driving SMARTS
The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods, trained on a combination of naturalistic AD data and open-source simulation platform SMARTS.
PedFormer: Pedestrian Behavior Prediction via Cross-Modal Attention Modulation and Gated Multitask Learning
To address this challenge, we propose a novel framework that relies on different data modalities to predict future trajectories and crossing actions of pedestrians from an ego-centric perspective.
LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction
Multi-agent trajectory prediction is a fundamental problem in autonomous driving.
Pedestrian Simulation: A Review
This article focuses on different aspects of pedestrian (crowd) modeling and simulation.
PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D
To this end, we propose a new pedestrian action prediction dataset created by adding per-frame 2D/3D bounding box and behavioral annotations to the popular autonomous driving dataset, nuScenes.
Bifold and Semantic Reasoning for Pedestrian Behavior Prediction
Our method benefits from 1) a bifold encoding approach where different data modalities are processed independently allowing them to develop their own representations, and jointly to produce a representation for all modalities using shared parameters; 2) a novel interaction modeling technique that relies on categorical semantic parsing of the scenes to capture interactions between target pedestrians and their surroundings; and 3) a bifold prediction mechanism that uses both independent and shared decoding of multimodal representations.
Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction
2) We introduce a new dataset that provides 3D bounding box and pedestrian behavioural annotations for the existing nuScenes dataset.
Multi-Modal Hybrid Architecture for Pedestrian Action Prediction
Pedestrian behavior prediction is one of the major challenges for intelligent driving systems in urban environments.
Deep Learning for Vision-based Prediction: A Survey
In addition, we discuss the common evaluation metrics and datasets used for vision-based prediction tasks.
Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs
To this end, we propose a solution for the problem of pedestrian action anticipation at the point of crossing.
Do Saliency Models Detect Odd-One-Out Targets? New Datasets and Evaluations
Furthermore, we investigate the effect of training state-of-the-art CNN-based saliency models on these types of stimuli and conclude that the additional training data does not lead to a significant improvement of their ability to find odd-one-out targets.
PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction
To date, only a few public datasets were proposed for the purpose of studying pedestrian behavior prediction in the context of intelligent driving.
Ranked #3 on
Trajectory Prediction
on JAAD
Attention-based Active Visual Search for Mobile Robots
In this paper, we propose a new model that actively extracts visual information via visual attention techniques and, in conjunction with a non-myopic decision-making algorithm, leads the robot to search more relevant areas of the environment.
Visual Attention and its Intimate Links to Spatial Cognition
It is almost universal to regard attention as the facility that permits an agent, human or machine, to give priority processing resources to relevant stimuli while ignoring the irrelevant.
Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice
To make it a reality, autonomous vehicles require the ability to communicate with other road users and understand their intentions.
Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice
Today, one of the major challenges that autonomous vehicles are facing is the ability to drive in urban environments.
The Effect of Color Space Selection on Detectability and Discriminability of Colored Objects
The results indicate that on average color space C1C2C3 followed by HSI and XYZ achieve the best time in searching for objects of various colors.
Integrating Three Mechanisms of Visual Attention for Active Visual Search
Algorithms for robotic visual search can benefit from the use of visual attention methods in order to reduce computational costs.
Joint Attention in Autonomous Driving (JAAD)
In this paper we present a novel dataset for a critical aspect of autonomous driving, the joint attention that must occur between drivers and of pedestrians, cyclists or other drivers.
Robotics
