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näkemys - Trajectory Prediction for Autonomous Driving
Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environments
This study proposes an innovative trajectory prediction model that integrates cognitive insights on perceived safety and dynamic decision-making to enhance the performance of autonomous driving systems in mixed autonomy environments.
Characterized Diffusion and Spatial-Temporal Interaction Network for Accurate Trajectory Prediction in Autonomous Driving
A novel generative model that integrates characterized diffusion and spatial-temporal interaction networks to accurately predict trajectories of vehicles in complex and dynamic traffic scenarios.
Optimal Trajectory Sampling from Heterogeneous Ensemble of Motion Forecasting Models
A novel method for generating optimal trajectories by combining predictions from an ensemble of heterogeneous trajectory prediction models, outperforming existing sampling techniques.
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