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Suppose in a stable urban traffic system populated only by human driven vehicles (HDVs), a given proportion (e.g. 10%) is replaced by a fleet of Connected and Autonomous Vehicles (CAVs), which share information and pursue a collective goal. Suppose these vehicles are centrally coordinated and differ from HDVs only by their collective capacities allowing them to make more efficient routing decisions before the travel on a given day begins. Suppose there is a choice between two routes and every day each driver makes a decision which route to take. Human drivers maximize their utility. CAVs might optimize different goals, such as the total travel time of the fleet. We show that in this plausible futuristic setting, the strategy CAVs are allowed to adopt may result in human drivers either benefitting or being systematically disadvantaged and urban networks becoming more or less optimal. Consequently, some regulatory measures might become indispensable.

In recent years, transformer-based architectures become the de facto standard for sequence modeling in deep learning frameworks. Inspired by the successful examples, we propose a causal visual-inertial fusion transformer (VIFT) for pose estimation in deep visual-inertial odometry. This study aims to improve pose estimation accuracy by leveraging the attention mechanisms in transformers, which better utilize historical data compared to the recurrent neural network (RNN) based methods seen in recent methods. Transformers typically require large-scale data for training. To address this issue, we utilize inductive biases for deep VIO networks. Since latent visual-inertial feature vectors encompass essential information for pose estimation, we employ transformers to refine pose estimates by updating latent vectors temporally. Our study also examines the impact of data imbalance and rotation learning methods in supervised end-to-end learning of visual inertial odometry by utilizing specialized gradients in backpropagation for the elements of SE$(3)$ group. The proposed method is end-to-end trainable and requires only a monocular camera and IMU during inference. Experimental results demonstrate that VIFT increases the accuracy of monocular VIO networks, achieving state-of-the-art results when compared to previous methods on the KITTI dataset. The code will be made available at https://github.com/ybkurt/VIFT., Comment: Accepted to ECCV 2024 2nd Workshop on Vision-Centric Autonomous Driving (VCAD)