This paper explores the concept of creating a"self"for self-driving cars through a homeostatic architecture designed to enhance their autonomy, safety, and efficiency. The proposed system integrates inward focused sensors to monitor the car's internal state, such as the condition of its metal bodywork, wheels, engine, and battery, establishing a baseline homeostatic state representing optimal functionality. Outward facing sensors, like cameras and LIDAR, are then interpreted via their impact on the car's homeostatic state by quantifying deviations from homeostasis. This contrasts with the approach of trying to make cars"see"reality in a similar way to humans and identify elements in their reality in the same way humans. Virtual environments would be leveraged to accelerate training. Additionally, cars are programmed to communicate and share experiences via blockchain technology, learning from each other's mistakes while maintaining individualized training models. A dedicated language for self-driving cars is proposed to enable nuanced interpretation and response to environmental data. This architecture allows self-driving cars to dynamically adjust their behavior based on internal and external feedback, promoting cooperation and continuous improvement. The study concludes by discussing the broader implications for AI development, potential real-world applications, and future research directions.

The need of a self for self-driving cars a theoretical model applying homeostasis to self driving

We present a simple, modular, and generic method that upsamples coarse 3D models by adding geometric and appearance details. While generative 3D models now exist, they do not yet match the quality of their counterparts in image and video domains. We demonstrate that it is possible to directly repurpose existing (pretrained) video models for 3D super-resolution and thus sidestep the problem of the shortage of large repositories of high-quality 3D training models. We describe how to repurpose video upsampling models, which are not 3D consistent, and combine them with 3D consolidation to produce 3D-consistent results. As output, we produce high quality Gaussian Splat models, which are object centric and effective. Our method is category agnostic and can be easily incorporated into existing 3D workflows. We evaluate our proposed SuperGaussian on a variety of 3D inputs, which are diverse both in terms of complexity and representation (e.g., Gaussian Splats or NeRFs), and demonstrate that our simple method significantly improves the fidelity of the final 3D models. Check our project website for details: supergaussian.github.io 

pdf/supergaussian-repurposing-video-models-for-3d-super-5190f4dfps.pdf

SuperGaussian: Repurposing Video Models for 3D Super Resolution

We present Sim-on-Wheels, a safe, realistic, and vehicle-in-loop framework to test autonomous vehicles' performance in the real world under safety-critical scenarios. Sim-on-wheels runs on a self-driving vehicle operating in the physical world. It creates virtual traffic participants with risky behaviors and seamlessly inserts the virtual events into images perceived from the physical world in real-time. The manipulated images are fed into autonomy, allowing the self-driving vehicle to react to such virtual events. The full pipeline runs on the actual vehicle and interacts with the physical world, but the safety-critical events it sees are virtual. Sim-on-Wheels is safe, interactive, realistic, and easy to use. The experiments demonstrate the potential of Sim-on-Wheels to facilitate the process of testing autonomous driving in challenging real-world scenes with high fidelity and low risk.

pdf/sim-on-wheels-physical-world-in-the-loop-simulation-for-self-2bpyzs82.pdf

Sim-on-Wheels: Physical World in the Loop Simulation for Self-Driving

Compared with gaze data collected by the GP3 eye-tracker, the webcam gaze shows noisier behavior in two perspectives. For illustration purposes, we randomly select three manual drive sessions from our dataset and visualize their cumulative heatmap as shown in Figure 1. First, the noisiness can be seen from the fact that the highest density region of the cumulative attention does not always locate around the center of the driving scene, which we refer to as the center shift issue. The center shift issue is unlikely to be the actual driver attention property, since the road ahead is mostly forward, and the eye fixations should be around the scene center. Second, even if the webcam heatmap is approximately located in the center of the driving scene, the gaze data is distributed more randomly than the eye-tracker gaze. To address the noisy behavior of the webcam data, in Section 5 of our paper, we propose a simple but effective baseline architecture that uses a coarseto-fine calibration procedure that handles the two perspectives of noisiness above respectively. Aside from the noisy pattern of the data distribution itself, the webcam-based eye-tracking API that we use is susceptible to the presence of mask and skin color due to its dependency on facial landmark detection output. In our dataset, we mask those frames that the facial landmark detection fails due to people wearing masks.

CoCAtt: A Cognitive-Conditioned Driver Attention Dataset (Supplementary Material)

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Sim-on-Wheels: Physical World in the Loop Simulation for Self-Driving

CoCAtt: A Cognitive-Conditioned Driver Attention Dataset (Supplementary Material)