Motion prediction and trajectory forecasting for autonomous driving — predicting future positions of vehicles, pedestrians and cyclists in complex traffic scenarios.
An autonomous driving research paper: Deep Learning for Vehicle Trajectory Prediction: A Comprehensive Review of Task Formulation, Spatiotemporal Interaction, Multimodal Generation, and Evaluation Frameworks.
Engineering 5.0 · Research 7.0 · Business 5.0
To address these shortcomings, we present SafeLane-VANET, a novel communication-aware framework for predicting safe arbitration behaviour during cooperative lane changes in mixed AV/Non-AV traffic.
Engineering 7.0 · Research 8.0 · Business 5.5
An autonomous driving research paper: INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: DeGOA: an optimized deep LSTM model for vehicle trajectory prediction.
Engineering 5.0 · Research 7.0 · Business 5.0
A trajectory prediction network model based on airport road operation rules is proposed to ensure the safety of autonomous vehicles driving in the airport flight area.
Engineering 5.0 · Research 7.0 · Business 5.0
Furthermore, we present a physically-based pipeline for generating Motion4D, the first synthetic 4D LiDAR dataset tailored for SRMP research, addressing the lack of synthetic motion datasets.
Engineering 5.5 · Research 8.0 · Business 5.5
In this paper, we introduce Text-Guided Representation for Instance Prediction (TGRIP), a novel framework that bridges this gap by injecting rich semantic priors into the instance prediction loop.
Engineering 7.5 · Research 8.5 · Business 5.0
We present a Reliable Context-Aware and Temporal Planning framework for Autonomous Driving (RCT-AD) that explicitly models feature quality and temporal consistency to support safer, more consistent planning.
Engineering 6.5 · Research 7.0 · Business 6.0
Experimental results demonstrate that our framework reduces average displacement error by 42% and Final Displacement Error by 40% compared to existing state-of-the-art models on ETH and UCY, while enabling near-real-time inference (0.003 s).
Engineering 5.0 · Research 8.0 · Business 5.0
Decentralized coordination at unsignalized intersections remains a persistent failure mode formodern autonomous driving policies when vehicle-to-everything (V2X) communication is unavail- able.
Engineering 5.0 · Research 8.0 · Business 6.0
Experiments on nuScenes and VIRAT validate our approach.
Engineering 6.0 · Research 7.5 · Business 5.5
We propose DriveTeach-VLA, a framework that explicitly teaches VLAs what to see and where to look.
Engineering 7.5 · Research 8.0 · Business 5.0
This paper presents SD-RouteFusion, a deployable end-to-end ego-trajectory prediction method that fuses a front-facing camera, vehicle kinematics, and a navigation route derived from a Standard Definition (SD) map.
Engineering 6.0 · Research 7.0 · Business 6.5
Because these metrics encourage conflicting behavior, we propose a paradigm change for trajectory forecasting: training models with metric-agnostic probabilistic objectives and treating metric optimization as a downstream task applied to the predictive distribution.
Engineering 5.5 · Research 8.0 · Business 6.0
We propose FAT, a foundation-model-augmented task-specific reasoning framework that treats collaboration as task decomposition rather than model replacement.
Engineering 5.0 · Research 8.5 · Business 5.0
To address these issues, we propose CylindTrack, a depth-aware cylindrical tracking-by-detection framework for panoramic MOT.
Engineering 6.5 · Research 7.0 · Business 5.0
In this paper, we propose a new iterative 3D position estimation algorithm (KGA).
Engineering 6.0 · Research 8.0 · Business 6.0
Autonomous driving systems rely on precise trajectory prediction to plan safe and efficient movement.
Engineering 5.0 · Research 7.0 · Business 5.0
In this work, we introduce a structured multi-level visual perturbation framework to analyze visual-behavior dependency in VLA-based driving models systematically.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we present SparseWorld, a lightweight world model that focuses on predicting only the critical layout of the scene, enabling efficient future forecasting for end-to-end driving systems.
Engineering 7.5 · Research 8.0 · Business 5.0