SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds. In particular, we wish to detect and categorize instances of interest, such as cars, pedestrians and cyclists. We formulate this problem as a point- wise classification problem, and propose an end-to-end pipeline called SqueezeSeg based on convolutional neural networks (CNN): the CNN takes a transformed LiDAR point cloud as input and directly outputs a point-wise label map, which is then refined by a conditional random field (CRF) implemented as a recurrent layer. Instance-level labels are then obtained by conventional clustering algorithms. Our CNN model is trained on LiDAR point clouds from the KITTI dataset, and our point-wise segmentation labels are derived from 3D bounding boxes from KITTI. To obtain extra training data, we built a LiDAR simulator into Grand Theft Auto V (GTA-V), a popular video game, to synthesize large amounts of realistic training data. Our experiments show that SqueezeSeg achieves high accuracy with astonishingly fast and stable runtime (8.7 ms per frame), highly desirable for autonomous driving applications. Furthermore, additionally training on synthesized data boosts validation accuracy on real-world data. Our source code and synthesized data will be open-sourced

SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

One major factor impeding more widespread adoption of deep neural networks (DNNs) is their lack of robustness, which is essential for safety-critical applications such as autonomous driving. This has motivated much recent work on adversarial attacks for DNNs, which mostly focus on pixel-level perturbations void of semantic meaning. In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks. To do this, we re-frame the adversarial attack problem as learning a distribution of parameters that always fools the agent. In the semantic case, our proposed adversary (denoted as BBGAN) is trained to sample parameters that describe the environment with which the black-box agent interacts, such that the agent performs its dedicated task poorly in this environment. We apply BBGAN on three different tasks, primarily targeting aspects of autonomous navigation: object detection, self-driving, and autonomous UAV racing. On these tasks, BBGAN can generate failure cases that consistently fool a trained agent.Comment: Accepted at AAAI'2

Multimodal 3D Object Detection from Simulated Pretraining

The need for simulated data in autonomous driving applications has become increasingly important, both for validation of pretrained models and for training new models. In order for these models to generalize to real-world applications, it is critical that the underlying dataset contains a variety of driving scenarios and that simulated sensor readings closely mimics real-world sensors. We present the Carla Automated Dataset Extraction Tool (CADET), a novel tool for generating training data from the CARLA simulator to be used in autonomous driving research. The tool is able to export high-quality, synchronized LIDAR and camera data with object annotations, and offers configuration to accurately reflect a real-life sensor array. Furthermore, we use this tool to generate a dataset consisting of 10 000 samples and use this dataset in order to train the 3D object detection network AVOD-FPN, with finetuning on the KITTI dataset in order to evaluate the potential for effective pretraining. We also present two novel LIDAR feature map configurations in Bird's Eye View for use with AVOD-FPN that can be easily modified. These configurations are tested on the KITTI and CADET datasets in order to evaluate their performance as well as the usability of the simulated dataset for pretraining. Although insufficient to fully replace the use of real world data, and generally not able to exceed the performance of systems fully trained on real data, our results indicate that simulated data can considerably reduce the amount of training on real data required to achieve satisfactory levels of accuracy.Comment: 12 pages, part of proceedings for the NAIS 2019 symposiu