Self-supervised feature learning enables perception systems to benefit from the vast raw data recorded by vehicle fleets worldwide. While video-level self-supervised learning approaches have shown strong generalizability on classification tasks, the potential to learn dense representations from sequential data has been relatively unexplored. In this work, we propose TempO, a temporal ordering pretext task for pre-training region-level feature representations for perception tasks. We embed each frame by an unordered set of proposal feature vectors, a representation that is natural for object detection or tracking systems, and formulate the sequential ordering by predicting frame transition probabilities in a transformer-based multi-frame architecture whose complexity scales less than quadratic with respect to the sequence length. Extensive evaluations on the BDD100K, nuImages, and MOT17 datasets show that our TempO pre-training approach outperforms single-frame self-supervised learning methods as well as supervised transfer learning initialization strategies, achieving an improvement of +0.7% in mAP for object detection and +2.0% in the HOTA score for multi-object tracking.
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；FZI Research Center for Information Technology（FZI信息技术研究中心）
In this paper, we present TEScalib, a novel extrinsic self-calibration approach of LiDAR and stereo camera using the geometric and photometric information of surrounding environments without any calibration targets for automated driving vehicles. Since LiDAR and stereo camera are widely used for sensor data fusion on automated driving vehicles, their extrinsic calibration is highly important. However, most of the LiDAR and stereo camera calibration approaches are mainly target-based and therefore time consuming. Even the newly developed targetless approaches in last years are either inaccurate…
Surround-view fisheye cameras are commonly used for near-field sensing in automated driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360° around the vehicle capturing the entire near-field region. Some primary use cases are automated parking, traffic jam assist, and urban driving. There are limited datasets and very little work on near-field perception tasks as the focus in automotive perception is on far-field perception. In contrast to far-field, surround-view perception poses additional challenges due to high precision object detection requirements of 10cm …
Technische Universität Braunschweig（布伦瑞克工业大学）；Volkswagen AG（大众汽车集团）
Automated driving has become a major topic of interest not only in the active research community but also in mainstream media reports. Visual perception of such intelligent vehicles has experienced large progress in the last decade thanks to advances in deep learning techniques but some challenges still remain. One such challenge is the detection of corner cases. They are unexpected and unknown situations that occur while driving. Conventional visual perception methods are often not able to detect them because corner cases have not been witnessed during training. Hence, their detection is high…
Occluded nuScenes: A Multi-Sensor Dataset for Evaluating Perception Robustness in Automated Driving
University of Limerick（利默里克大学）；Data Driven Computer Engineering Research Centre（数据驱动计算机工程研究中心）；Lero, The Irish Software Research Centre（Lero爱尔兰软件研究中心）；Valeo Vision Systems（Valéo视觉系统）；Queen Mary University of London（伦敦女王学院）
Lane detection is an essential part of the perception sub-architecture of any automated driving (AD) or advanced driver assistance system (ADAS). When focusing on low-cost, large scale products for automated driving, model-driven approaches for the detection of lane markings have proven good performance. More recently, data-driven approaches have been proposed that target the drivable area / freespace mainly in inner-city applications. Focus of these approaches is less on lane-based driving due to the fact that the lane concept does not fully apply in unstructured, residential inner-city envir…
University of Alberta（阿尔伯塔大学）；Valeo Vision Systems（法雷奥视觉系统公司）
Semantic segmentation was seen as a challenging computer vision problem few years ago. Due to recent advancements in deep learning, relatively accurate solutions are now possible for its use in automated driving. In this paper, the semantic segmentation problem is explored from the perspective of automated driving. Most of the current semantic segmentation algorithms are designed for generic images and do not incorporate prior structure and end goal for automated driving. First, the paper begins with a generic taxonomic survey of semantic segmentation algorithms and then discusses how it fits …
Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, visual odometry, etc. The main flavors of neural networks which are used commonly are convolutional (CNN) and recurrent (RNN). In spite of rapid progress in embedded processors, power consumption and cost is still a bottleneck. Spiking Neural Networks (SNNs) are gradually progressing to achieve low-power event-driven hardware architecture which has a potential for high efficiency. In this paper, we explore the role of …
RWTH Aachen University（亚琛工业大学）；Institute for Automotive Engineering（汽车工程研究所）
Safety assurance of automated driving systems must consider uncertain environment perception. This paper reviews literature addressing how perception testing is realized as part of safety assurance. We focus on testing for verification and validation purposes at the interface between perception and planning, and structure our analysis along the three axes 1) test criteria and metrics, 2) test scenarios, and 3) reference data. Furthermore, the analyzed literature includes related safety standards, safety-independent perception algorithm benchmarking, and sensor modeling. We find that the realiz…
NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving
ARC Training Centre for Automated Vehicles in Rural and Remote Regions (AVR3)（澳大利亚农村及偏远地区自动驾驶车辆ARC培训中心）；Université Gustave Eiffel（Gustave Eiffel大学）；Queensland University of Technology (QUT)（昆士兰科技大学）
The goal of this paper is to generate simulations with real-world collision scenarios for training and testing autonomous vehicles. We use numerous dashcam crash videos uploaded on the internet to extract valuable collision data and recreate the crash scenarios in a simulator. We tackle the problem of extracting 3D vehicle trajectories from videos recorded by an unknown and uncalibrated monocular camera source using a modular approach. A working architecture and demonstration videos along with the open-source implementation are provided with the paper.
Valeo Troy（法雷奥特洛伊）；Valeo Vision Systems（法雷奥视觉系统）；University of Michigan-Dearborn（密歇根大学迪尔伯恩分校）
Decision making in automated driving is highly specific to the environment and thus semantic segmentation plays a key role in recognizing the objects in the environment around the car. Pixel level classification once considered a challenging task which is now becoming mature to be productized in a car. However, semantic annotation is time consuming and quite expensive. Synthetic datasets with domain adaptation techniques have been used to alleviate the lack of large annotated datasets. In this work, we explore an alternate approach of leveraging the annotations of other tasks to improve semant…
Semantic segmentation remains a computationally intensive algorithm for embedded deployment even with the rapid growth of computation power. Thus efficient network design is a critical aspect especially for applications like automated driving which requires real-time performance. Recently, there has been a lot of research on designing efficient encoders that are mostly task agnostic. Unlike image classification and bounding box object detection tasks, decoders are computationally expensive as well for semantic segmentation task. In this work, we focus on efficient design of the segmentation de…
Robert Bosch GmbH（罗伯特·博世股份有限公司）；Research Center for Information Technology (FZI)（信息技术研究中心（FZI））
Making informed driving decisions requires reliable prediction of other vehicles' trajectories. In this paper, we present a novel learned multi-modal trajectory prediction architecture for automated driving. It achieves kinematically feasible predictions by casting the learning problem into the space of accelerations and steering angles -- by performing action-space prediction, we can leverage valuable model knowledge. Additionally, the dimensionality of the action manifold is lower than that of the state manifold, whose intrinsically correlated states are more difficult to capture in a learne…
An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions
Stuttgart Media University（斯图加特媒体大学）；Fraunhofer-Gesellschaft e.V.（弗劳恩霍夫协会）；Fraunhofer EMI（弗劳恩霍夫应用信息技术研究所）；Fraunhofer IVI（弗劳恩霍夫交通与基础设施系统研究所）；Fraunhofer IOSB（弗劳恩霍夫光学、微系统和图像处理研究所）；GOTECH GmbH（高泰克有限公司）；Technische Universität München（慕尼黑工业大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；understandAI GmbH（安代人工智能有限公司）；Porsche Engineering Group GmbH（保时捷工程集团有限公司）
