2 research outputs found
Diffusion-Based Particle-DETR for BEV Perception
The Bird-Eye-View (BEV) is one of the most widely-used scene representations
for visual perception in Autonomous Vehicles (AVs) due to its well suited
compatibility to downstream tasks. For the enhanced safety of AVs, modeling
perception uncertainty in BEV is crucial. Recent diffusion-based methods offer
a promising approach to uncertainty modeling for visual perception but fail to
effectively detect small objects in the large coverage of the BEV. Such
degradation of performance can be attributed primarily to the specific network
architectures and the matching strategy used when training. Here, we address
this problem by combining the diffusion paradigm with current state-of-the-art
3D object detectors in BEV. We analyze the unique challenges of this approach,
which do not exist with deterministic detectors, and present a simple technique
based on object query interpolation that allows the model to learn positional
dependencies even in the presence of the diffusion noise. Based on this, we
present a diffusion-based DETR model for object detection that bears
similarities to particle methods. Abundant experimentation on the NuScenes
dataset shows equal or better performance for our generative approach, compared
to deterministic state-of-the-art methods. Our source code will be made
publicly available
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Towards Conscious RL Agents By Construction
The nature of consciousness has been a long-debated concept related to human cognition and self-understanding. As AI systems become more capable and autonomous, it is an increasingly pressing matter whether they can be called conscious. In line with narrative-based theories, here we present a simple but concrete computational criterion for consciousness grounded in the querying of a virtual self-representation. We adopt a reinforcement learning (RL) setting and implement these ideas in SubjectZero, a planning-based deep RL agent which has an explicit virtual self-model and whose architecture draws similarities to multiple prominent consciousness theories. Being able to self-localize, simulate the world, and model its own internal state, it can support a primitive virtual narrative, the quality of which depends on the number of abstractions that the underlying generative model sustains. Task performance still ultimately depends on the modeling capabilities of the agent where intelligence, understood simply as the ability to model complicated relationships, is what matters