TGRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving
Bird's-Eye View (BEV) end-to-end instance prediction has emerged as a robust paradigm for autonomous driving perception, effectively mitigating the error propagation inherent in traditional modular pipelines. However, current state-of-the-art approaches rely predominantly on geometric supervision, such as occupancy regression and optical flow, effectively treating scene agents as generic moving obstacles. This absence of explicit semantic awareness imposes limitations on the capacity of the model to solve ambiguities in complex scenarios, particularly those where object-specific behavior is essential for accurate forecasting (e.g. overtaking, intersections). In this paper, we introduce Te...
| Authors | Miguel Antunes-García, Santiago Montiel-Marín, Fabio Sánchez-García et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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