Lane detection have made significant progress in recent years, but there is not a unified architecture for its two sub-tasks: 2D lane detection and 3D lane detection. To fill this gap, we introduce BézierFormer, a unified 2D and 3D lane detection architecture based on Bézier curve lane representation. BézierFormer formulate queries as Bézier control points and incorporate a novel Bézier curve attention mechanism. This attention mechanism enables comprehensive and accurate feature extraction for slender lane curves via sampling and fusing multiple reference points on each curve. In addition, we propose a novel Chamfer IoU-based loss which is more suitable for the Bézier control points regression. The state-of-the-art performance of BézierFormer on widely-used 2D and 3D lane detection benchmarks verifies its effectiveness and suggests the worthiness of further exploration.

This paper introduces rotation-equivariance as a self-supervisor to train inertial odometry models. We demonstrate that the self-supervised scheme provides a powerful supervisory signal at training phase as well as at inference stage. It reduces the reliance on massive amounts of labeled data for training a robust model and makes it possible to update the model using various unlabeled data. Further, we propose adaptive Test-Time Training (TTT) based on uncertainty estimations in order to enhance the generalizability of the inertial odometry to various unseen data. We show in experiments that the Rotation-equivariance-supervised Inertial Odometry (RIO) trained with 30% data achieves on par performance with a model trained with the whole dataset. Adaptive TTT improves models' performance in all cases and makes more than 25% improvements under several scenarios. We release our code and dataset at this website.

Gait training is widely used to treat gait abnormalities. Traditional gait measurement systems are limited to instrumented laboratories. Even though gait measurements can be made in these settings, it is challenging to estimate gait parameters robustly in real-time for gait rehabilitation, especially when walking over-ground. In this paper, we present a novel approach to track the continuous gait cycle during overground walking outside the laboratory. In this approach, we instrument standard footwear with a sensorized insole and an inertial measurement unit. Artificial neural networks are used on the raw data obtained from the insoles and IMUs to compute the continuous percentage of the gait cycle for the entire walking session. We show in this paper that when tested with novel subjects, we can predict the gait cycle with a Root Mean Square Error (RMSE) of 7.2%. The onset of each cycle can be detected within an RMSE time of 41.5 ms with a 99% detection rate. The algorithm was tested with 18840 strides collected from 24 adults. In this paper, we tested a combination of fully-connected layers, an Encoder-Decoder using convolutional layers, and recurrent layers to identify an architecture that provided the best performance.