Congratulations Graduates!

David Paz, Cassie Qiu, Jiaming Hu, Henry Zhang, and Michelle Sit completed their respective Master of Science degrees. David, Cassie, Jiaming and Henry will be continuing for their PhD, while Michelle will be starting a new career in the industry. Congratulations everyone!

Congratulations Dr. Wang!

Shengye Wang recently defended his PhD thesis titled “Reliability Engineering for Long-term Autonomous Service Robots”. He will be starting a new adventure in his life at Waymo. Congratulations Shengye!

“Looking at the Right Stuff” – Guided Semantic-Gaze for Autonomous Driving

Abstract

In recent years, predicting driver’s focus of attention has been a very active area of research in the autonomous driving community. Unfortunately, existing state-of-the-art techniques achieve this by relying only on human gaze information, thereby ignoring scene semantics. We propose a novel Semantics Augmented GazE (SAGE) detection approach that captures driving specific contextual information, in addition to the raw gaze. Such a combined attention mechanism serves as a powerful tool to focus on the relevant regions in an image frame in order to make driving both safe and efficient. Using this, we design a complete saliency prediction framework – SAGE-Net, which modifies the initial prediction from SAGE by taking into account vital aspects such as distance to objects (depth), ego vehicle speed, and pedestrian crossing intent. Exhaustive experiments conducted through four popular saliency algorithms show that on 49/56 (87.5%) cases – considering both the overall dataset and crucial driving scenarios, SAGE outperforms existing techniques without any additional computational overhead during the training process. The augmented dataset along with the relevant code are available as part of the supplementary material.

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