16 research outputs found
Interactive Object Segmentation using Binary Inputs
Every day, humans use their vision to process millions of pixels and select regions of interest. This task of highlighting and grouping pixels of interest in a scene is called image segmentation, and it is a fundamental method that humans use to communicate with each other ideas, concepts, and emotions. We introduce a method derived from feedback information theory that allows individuals with motor control disabilities to perform image segmentation using only binary inputs and a simple encoding scheme. We propose two versions of our algorithm, and evaluate their ability to specify desired regions for the user with restricted inputs and noise on large, publicly available image data sets. We also compare our method to the previous best algorithm, developed by Rupprecht et al.Undergraduat
Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation
Self-driving vehicles (SDVs) must be rigorously tested on a wide range of
scenarios to ensure safe deployment. The industry typically relies on
closed-loop simulation to evaluate how the SDV interacts on a corpus of
synthetic and real scenarios and verify it performs properly. However, they
primarily only test the system's motion planning module, and only consider
behavior variations. It is key to evaluate the full autonomy system in
closed-loop, and to understand how variations in sensor data based on scene
appearance, such as the shape of actors, affect system performance. In this
paper, we propose a framework, Adv3D, that takes real world scenarios and
performs closed-loop sensor simulation to evaluate autonomy performance, and
finds vehicle shapes that make the scenario more challenging, resulting in
autonomy failures and uncomfortable SDV maneuvers. Unlike prior works that add
contrived adversarial shapes to vehicle roof-tops or roadside to harm
perception only, we optimize a low-dimensional shape representation to modify
the vehicle shape itself in a realistic manner to degrade autonomy performance
(e.g., perception, prediction, and motion planning). Moreover, we find that the
shape variations found with Adv3D optimized in closed-loop are much more
effective than those in open-loop, demonstrating the importance of finding
scene appearance variations that affect autonomy in the interactive setting.Comment: CoRL 2023. Project page: https://waabi.ai/adv3d