More Like this

1. DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

   https://doi.org/10.14722/autosec.2022.23032  

   Muller, Raymond (January 2022, Workshop on Automotive and Autonomous Vehicle Security (AutoSec))

   With emerging vision-based autonomous driving (AD) systems, it becomes increasingly important to have datasets to evaluate their correct operation and identify potential security flaws. However, when collecting a large amount of data, either human experts manually label potentially hundreds of thousands of image frames or systems use machine learning algorithms to label the data, with the hope that the accuracy is good enough for the application. This can become especially problematic when tracking the context information, such as the location and velocity of surrounding objects, useful to evaluate the correctness and improve stability and robustness of the AD systems. In this paper, we introduce DRIVETRUTH, a data collection framework built on CARLA, an open-source simulator for AD research, which constructs datasets with automatically generated accurate object labels, bounding boxes of objects and their contextual information through accessing simulation state and using semantic LiDAR raycasts. By leveraging the actual state of the simulation and the agents within it, we guarantee complete accuracy in all labels and gathered contextual information. Further, the use of the simulator provides easily collecting data in diverse environmental conditions and agent behaviors, with lighting, weather, and traffic behavior being configurable within the simulation. Through this effort, we provide users a means to extracting actionable simulated data from CARLA to test and explore attacks and defenses for AD systems. 

   more » « less
2. An Unsupervised Approach for Simultaneous Visual Odometry and Single Image Depth Estimation

   https://doi.org/10.1109/IJCNN55064.2022.9892294  

   Lu, Yawen; Lu, Guoyu (January 2022, IEEE International Joint Conference on Neural Network (IJCNN))

   Visual odometry (VO) and single image depth estimation are critical for robot vision, 3D reconstruction, and camera pose estimation that can be applied to autonomous driving, map building, augmented reality and many other applications. Various supervised learning models have been proposed to train the VO or single image depth estimation framework for each targeted scene to improve the performance recently. However, little effort has been made to learn these separate tasks together without requiring the collection of a significant number of labels. This paper proposes a novel unsupervised learning approach to simultaneously perceive VO and single image depth estimation. In our framework, either of these tasks can benefit from each other through simultaneously learning these two tasks. We correlate these two tasks by enforcing depth consistency between VO and single image depth estimation. Based on the single image depth estimation, we can resolve the most common and challenging scaling issue of monocular VO. Meanwhile, through training from a sequence of images, VO can enhance the single image depth estimation accuracy. The effectiveness of our proposed method is demonstrated through extensive experiments compared with current state-of-the-art methods on the benchmark datasets. 

   more » « less
3. Social ODE: Multi-agent Trajectory Forecasting with Neural Ordinary Differential Equations.

   https://doi.org/10.1007/978-3-031-20047-2_13  

   Wen, S.; Wang, H.; Metaxas, D. (January 2022, Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science)

   Avidan, S.; Brostow, G.; Cissé, M.; Farinella, G.M.; Hassner, T. (Ed.)

   Wen, S., Wang, H., Metaxas, D. (2022). Social ODE: Multi-agent Trajectory Forecasting with Neural Ordinary Differential Equations. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13682. Springer, Cham. https://doi.org/10.1007/978-3-031-20047-2_13 Multi-agent trajectory forecasting has recently attracted a lot of attention due to its widespread applications including autonomous driving. Most previous methods use RNNs or Transformers to model agent dynamics in the temporal dimension and social pooling or GNNs to model interactions with other agents; these approaches usually fail to learn the underlying continuous temporal dynamics and agent interactions explicitly. To address these problems, we propose Social ODE which explicitly models temporal agent dynamics and agent interactions. Our approach leverages Neural ODEs to model continuous temporal dynamics, and incorporates distance, interaction intensity, and aggressiveness estimation into agent interaction modeling in latent space. We show in extensive experiments that our Social ODE approach compares favorably with state-of-the-art, and more importantly, can successfully avoid sudden obstacles and effectively control the motion of the agent, while previous methods often fail in such cases. 

   more » « less
4. Teaching invariance using priviledged mediation information

   Zapzalka, Dylan; Makar, Maggie (December 2024, Causal representation learning workshop at NeurIPS)

   The performance of deep neural networks often deteriorates in out-of-distribution settings due to relying on easy-to-learn but unreliable spurious associations known as shortcuts. Recent work attempting to mitigate shortcut learning relies on a priori knowledge of what the shortcut is and requires a strict overlap assumption with respect to the shortcut and the labels. In this paper, we present a causally-motivated teacher-student framework that encourages invariance to all shortcuts by leveraging privileged mediation information. The Teaching Invariance using Privileged Mediation Information (TIPMI) framework distills knowledge from a counterfactually invariant teacher trained using privileged mediation information to a student predictor that uses non-privileged features. We analyze the theoretical properties of our proposed estimator, showing that TIPMI promotes invariance to multiple unknown shortcuts and has better finite-sample efficiency. We empirically verify our theoretical findings by showing that TIPMI outperforms several state-of-the-art methods on two vision datasets and one language dataset. 

   more » « less
5. Learning to Drive Anywhere

   Zhu, Ruizhao; Huang, Peng; Ohn-Bar, Eshed; Saligrama, Venkatesh (November 2023, Conference on Robot Learning)

   Human drivers can seamlessly adapt their driving decisions across geographical locations with diverse conditions and rules of the road, e.g., left vs. right-hand traffic. In contrast, existing models for autonomous driving have been thus far only deployed within restricted operational domains, i.e., without accounting for varying driving behaviors across locations or model scalability. In this work, we propose AnyD, a single geographically-aware conditional imitation learning (CIL) model that can efficiently learn from heterogeneous and globally distributed data with dynamic environmental, traffic, and social characteristics. Our key insight is to introduce a high-capacity geo-location-based channel attention mechanism that effectively adapts to local nuances while also flexibly modeling similarities among regions in a data-driven manner. By optimizing a contrastive imitation objective, our proposed approach can efficiently scale across the inherently imbalanced data distributions and location-dependent events. We demonstrate the benefits of our AnyD agent across multiple datasets, cities, and scalable deployment paradigms, i.e., centralized, semi-supervised, and distributed agent training. Specifically, AnyD outperforms CIL baselines by over 14% in open-loop evaluation and 30% in closed-loop testing on CARLA. 

   more » « less