Anticipating driver intention is an important task when vehicles of mixed and varying levels of human/machine autonomy share roadways. Driver intention can be leveraged to improve road safety, such as warning surrounding vehicles in the event the driver is attempting a dangerous maneuver. In this work, we propose a novel method of utilizing both in-cabin and external camera data to improve state-of-the-art performance in predicting future driver actions. Compared to existing methods, our approach explicitly extracts object and road-level features from external camera data, which we demonstrate are important features for predicting driver intention. Using our handcrafted features as inputs for both a transformer and a long-short-term-memory-based architecture, we empirically show that jointly utilizing in-cabin and external features improves performance compared to using in-cabin features alone. Furthermore, our models predict driver maneuvers more accurately and sooner than existing approaches, with an accuracy of 87.5% and an average prediction time of 4.35 seconds before the maneuver takes place. We release our model configurations and training scripts on https://github.com/ykung83/Driver-Intent-Prediction.

Recently, humanoid robots have made significant advances in their ability to perform challenging tasks due to the deployment of Reinforcement Learning (RL), however, the inherent complexity of humanoid robots, including the difficulty of designing complicated reward functions and training entire sophisticated systems, still poses a notable challenge. To conquer these challenges, after many iterations and in-depth investigations, we have meticulously developed a full-size humanoid robot, "Adam", whose innovative structural design greatly improves the efficiency and effectiveness of the imitation learning process. In addition, we have developed a novel imitation learning framework based on an adversarial motion prior, which applies not only to Adam but also to humanoid robots in general. Using the framework, Adam can exhibit unprecedented human-like characteristics in locomotion tasks. Our experimental results demonstrate that the proposed framework enables Adam to achieve human-comparable performance in complex locomotion tasks, marking the first time that human locomotion data has been used for imitation learning in a full-size humanoid robot. For more video demonstrations, please visit our YouTube channel: https://www.youtube.com/watch?v=7hK2ySYBa1I

We introduce the UT Campus Object Dataset (CODa), a mobile robot egocentric perception dataset collected on the University of Texas Austin Campus. Our dataset contains 8.5 hours of multimodal sensor data from 3D LiDAR, stereo RGB and RGBD cameras, and a 9-DOF IMU. CODa contains 58 minutes of ground-truth annotations containing 1.3 million 3D bounding boxes with instance IDs for 53 semantic classes, 5000 frames of 3D semantic annotations for urban terrain, and pseudo-ground truth localization. We repeatedly traverse identical geographic regions for diverse indoor and outdoor areas, weather conditions, and times of the day. Using CODa, we empirically demonstrate that: 1) 3D object detection performance improves in urban settings when trained using CODa compared to existing datasets, 2) sensor-specific fine-tuning increases 3D object detection accuracy and 3) pretraining on CODa improves cross-dataset 3D object detection performance in urban settings compared to pretraining on AV datasets. We release benchmarks for 3D object detection and 3D semantic segmentation, with future plans for additional tasks. We publicly release CODa on the Texas Data Repository [1], pre-trained models, dataset development package, and interactive dataset viewer1. We expect CODa to be a valuable dataset for egocentric perception and planning for navigation in urban environments.

Robotic perception is currently at a cross-roads between modern methods, which operate in an efficient latent space, and classical methods, which are mathematically founded and provide interpretable, trustworthy results. In this paper, we introduce a Convolutional Bayesian Kernel Inference (Con-vBKI) layer which learns to perform explicit Bayesian inference within a depthwise separable convolution layer to maximize efficency while maintaining reliability simultaneously. We apply our layer to the task of real-time 3D semantic mapping, where we learn semantic-geometric probability distributions for LiDAR sensor information and incorporate semantic predictions into a global map. We evaluate our network against state-of-the-art semantic mapping algorithms on the KITTI data set, demonstrating improved latency with comparable semantic label inference results.