Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
V Bharilya, N Kumar - Vehicular Communications, 2024 - Elsevier
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
Dream: Decentralized real-time asynchronous probabilistic trajectory planning for collision-free multi-robot navigation in cluttered environments
B Şenbaşlar, GS Sukhatme - IEEE Transactions on Robotics, 2024 - ieeexplore.ieee.org
Collision-free navigation in cluttered environments with static and dynamic obstacles is
essential for many multirobot tasks. Dynamic obstacles may also be interactive, ie, their …
essential for many multirobot tasks. Dynamic obstacles may also be interactive, ie, their …
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving
Diffusion models excel at modeling complex and multimodal trajectory distributions for
decision-making and control. Reward-gradient guided denoising has been recently …
decision-making and control. Reward-gradient guided denoising has been recently …
Efficient baselines for motion prediction in autonomous driving
Motion Prediction (MP) of multiple surroundings agents is a crucial task in arbitrarily complex
environments, from simple robots to Autonomous Driving Stacks (ADS). Current techniques …
environments, from simple robots to Autonomous Driving Stacks (ADS). Current techniques …
Diverse controllable diffusion policy with signal temporal logic
Generating realistic simulations is critical for autonomous system applications such as self-
driving and human-robot interactions. However, driving simulators nowadays still have …
driving and human-robot interactions. However, driving simulators nowadays still have …
Diffusion-es: Gradient-free planning with diffusion for autonomous driving and zero-shot instruction following
Diffusion models excel at modeling complex and multimodal trajectory distributions for
decision-making and control. Reward-gradient guided denoising has been recently …
decision-making and control. Reward-gradient guided denoising has been recently …
Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network
Trajectory prediction is crucial for autonomous driving, as it aims to forecast the future
movements of traffic participants. Traditional methods usually perform holistic inference on …
movements of traffic participants. Traditional methods usually perform holistic inference on …
MS-Net: A Multi-Path Sparse Model for Motion Prediction in Multi-Scenes
The multi-modality and stochastic characteristics of human behavior make motion prediction
a highly challenging task, which is critical for autonomous driving. While deep learning …
a highly challenging task, which is critical for autonomous driving. While deep learning …
OLiVia-Nav: An Online Lifelong Vision Language Approach for Mobile Robot Social Navigation
Service robots in human-centered environments such as hospitals, office buildings, and long-
term care homes need to navigate while adhering to social norms to ensure the safety and …
term care homes need to navigate while adhering to social norms to ensure the safety and …
Multi-modal trajectory forecasting with Multi-scale Interactions and Multi-pseudo-target Supervision
Trajectory forecasting is crucial for the advancement of autonomous vehicles. While much
progress has been made, extant approaches often fall short in accounting for intricate social …
progress has been made, extant approaches often fall short in accounting for intricate social …