Motion prediction and trajectory forecasting for autonomous driving — predicting future positions of vehicles, pedestrians and cyclists in complex traffic scenarios.
To address this issue, we propose the Interaction-Aware Diffusion Model (IADM), a novel diffusion-based framework considering both human motions and surrounding scene layout by treating the social and scene interactions as conditions in the parameterized reverse Markov chain.
Engineering 5.0 · Research 8.0 · Business 5.0
We present a conceptual framework for Collaborative Joint Perception and Prediction (Co-P&P) that improves motion prediction of surrounding road users, thereby enhancing situational awareness in complex and dynamic traffic environments.
Engineering 6.0 · Research 7.0 · Business 6.0
We propose a Neural Ordinary Differential Equation (Neural ODE)-based framework that formulates single-frame prediction as continuous-time motion-state forecasting.
Engineering 5.0 · Research 7.0 · Business 5.0
We present SimWAM, a simple yet effective WAM that leverages future-video prediction as a training-time supervision signal.
Engineering 7.5 · Research 8.0 · Business 5.0
This paper proposes a mobility-aware predictive task offloading framework designed for reliable edge communications in future 6G-inspired vehicular network environments.
Engineering 5.5 · Research 7.0 · Business 6.0
To address these challenges, we present NSF-HRPT, a novel framework that combines learning-based perception with structured reasoning for quantitative risk assessment.
Engineering 5.5 · Research 8.5 · Business 5.5
An autonomous driving research paper: Shape-Adaptive Colour-based Single Object Tracking with Occlusion Recovery.
Engineering 5.0 · Research 7.0 · Business 5.0
To address this, this paper presents a multimodal trajectory prediction method combining kinematics with dynamic interaction features.
Engineering 5.0 · Research 7.0 · Business 5.0
This paper presents CogSig-Mamba, a model that produces causally validated temporal explanations alongside trajectory predictions.
Engineering 5.0 · Research 7.0 · Business 5.0
We propose SIPTraj, a map-free trajectory prediction framework that jointly addresses scene grounding and physical feasibility.
Engineering 7.5 · Research 7.0 · Business 5.0
We propose Li-ViP3D++, a query-based multimodal PnP framework that introduces Query-Gated Deformable Fusion (QGDF) to integrate multi-view RGB and LiDAR in query space.
Engineering 6.0 · Research 7.0 · Business 5.0
To enhance unmanned ground vehicle (UGV) intelligence in smart cities, disaster rescue, and infrastructure inspection, this paper investigates the collaborative optimization of multimodal fusion end-to-end architectures.
Engineering 6.5 · Research 7.0 · Business 5.5
We present PRISA, a modular infrastructure LiDAR framework leveraging privacy-preserving, low-light-robust roadside sensors for long-term traffic observation and real-time risk detection at the edge.
Engineering 5.5 · Research 7.0 · Business 5.0
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