Framework improves key object detection from driver perspective
A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction
Computer Vision and Pattern Recognition
Summary
Knowing which nearby objects are most important helps self-driving cars stay safe. The paper presents a two-step method that first predicts how objects around the car might behave based on the car’s viewpoint. Then it uses space and time information to decide which objects need the car’s attention most. The authors show their approach works better at finding critical objects in busy driving situations.
What this means in practice
- •For autonomous vehicle engineers: Improve object prioritization systems by integrating behavior predictions relative to the ego-vehicle’s view.
- •For driver assistance system designers: Enhance alerts by focusing on objects likely to affect the driver based on predicted states and spatial context.
Authors
Shihong Ling, Yue Wan, Xiaowei Jia, Na Du
Abstract
This paper presents a novel framework designed to enhance key object identification in autonomous driving. Existing methods primarily focus on either detecting objects independently or leveraging visual relationships, but they do not explicitly consider the ego vehicle's perspective in determining object importance. To address this gap, we propose a structured approach that integrates a virtual ego-vehicle representation and a modular object state predictor, enabling a more accurate estimation of object behaviors relative to the ego-vehicle. Subsequently, our framework employs spatial-temporal reasoning to refine key object identification, prioritizing objects based on their states and relative spatial information rather than relying solely on visual relationships. Experimental results on real-world driving datasets demonstrate the effectiveness of our approach in accurately detecting critical objects in complex traffic environments.