无人驾驶相关论文速递。论文速递信息见Issues,可在issue下进行讨论、交流 、学习:smile: :smile: :smile:
无人驾驶相关学者信息 见 scholars in Autonomous Vehicle
CVPR2019 无人驾驶相关论文 见CVPR2019 😊 😊 😊
以下论文将大致按照下图无人驾驶系统系统架构来整理。
注：以下统计的时间为在Arxiv提交的时间
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PointPillars: Fast Encoders for Object Detection from Point Clouds, :trophy: SOTA for Birds Eye View Object Detection on KITTI Cyclists Moderate
作者：Alex H. Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, Oscar Beijbom
日期： 2018-12-14
代码：traveller59/second.pytorch⭐ 228
摘要：Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline. Recent literature suggests two types of encoders; fixed encoders tend to be fast but sacrifice accuracy, while encoders that are learned from data are more accurate, but slower. In this work we propose PointPillars, a novel encoder which utilizes PointNets to learn a representation of point clouds organized in vertical columns (pillars). While the encoded features can be used with any standard 2D convolutional detection architecture, we further propose a lean downstream network. Extensive experimentation shows that PointPillars outperforms previous encoders with respect to both speed and accuracy by a large margin. Despite only using lidar, our full detection pipeline significantly outperforms the state of the art, even among fusion methods, with respect to both the 3D and bird's eye view KITTI benchmarks. This detection performance is achieved while running at 62 Hz: a 2 - 4 fold runtime improvement. A faster version of our method matches the state of the art at 105 Hz. These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds. -
Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN, IEEE International Conference On Big Data Cup 2018(2018年IEEE国际大数据杯会议的道路损伤检测和分类挑战)
作者：Janpreet Singh, Shashank Shekhar
时间：2018-11-12
代码：sshkhr/BigDataCup18_Submission
摘要: This paper summarizes the design, experiments and results of our solution to the Road Damage Detection and Classification Challenge held as part of the 2018 IEEE International Conference On Big Data Cup. Automatic detection and classification of damage in roads is an essential problem for multiple applications like maintenance and autonomous driving. We demonstrate that convolutional neural net based instance detection and classfication approaches can be used to solve this problem. In particular we show that Mask-RCNN, one of the state-of-the-art algorithms for object detection, localization and instance segmentation of natural images, can be used to perform this task in a fast manner with effective results. We achieve a mean F1 score of 0.528 at an IoU of 50% on the task of detection and classification of different types of damages in real-world road images acquired using a smartphone camera and our average inference time for each image is 0.105 seconds on an NVIDIA GeForce 1080Ti graphic card. The code and saved models for our approach can be found here : this https URL Submission -
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving, :trophy: SOTA for KITTI(2016)
作者：Bichen Wu, Alvin Wan, Forrest Iandola, Peter H. Jin, Kurt Keutzer
日期：2017-11-29（2016-12-04 v1版本）
代码: TensorFLow:BichenWuUCB/squeezeDet ⭐631
摘要:Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment. In this work, we propose SqueezeDet, a fully convolutional neural network for object detection that aims to simultaneously satisfy all of the above constraints. In our network we use convolutional layers not only to extract feature maps, but also as the output layer to compute bounding boxes and class probabilities. The detection pipeline of our model only contains a single forward pass of a neural network, thus it is extremely fast. Our model is fully-convolutional, which leads to small model size and better energy efficiency. Finally, our experiments show that our model is very accurate, achieving state-of-the-art accuracy on the KITTI benchmark.
- A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes,
:trophy: SOTA for Image-to-Image Translation on SYNTHIA-to-Cityscapes.
作者:Yang Zhang, Philip David, Hassan Foroosh, Boqing Gong
日期：2019-01-10（2018-12-24 v1版本）
代码：YangZhang4065/AdaptationSeg⭐68
摘要:During the last half decade, convolutional neural networks (CNNs) have triumphed over semantic segmentation, which is one of the core tasks in many applications such as autonomous driving and augmented reality. However, to train CNNs requires a considerable amount of data, which is difficult to collect and laborious to annotate. Recent advances in computer graphics make it possible to train CNNs on photo-realistic synthetic imagery with computer-generated annotations. Despite this, the domain mismatch between the real images and the synthetic data hinders the models' performance. Hence, we propose a curriculum-style learning approach to minimizing the domain gap in urban scene semantic segmentation. The curriculum domain adaptation solves easy tasks first to infer necessary properties about the target domain; in particular, the first task is to learn global label distributions over images and local distributions over landmark superpixels. These are easy to estimate because images of urban scenes have strong idiosyncrasies (e.g., the size and spatial relations of buildings, streets, cars, etc.). We then train a segmentation network, while regularizing its predictions in the target domain to follow those inferred properties. In experiments, our method outperforms the baselines on two datasets and two backbone networks. We also report extensive ablation studies about our approach.
- Online Temporal Calibration for Monocular Visual-Inertial Systems,
SOTA，IROS 2018,IMU和（单目）摄像头融合的校正方法，用来校准IMU和相机之间的时间偏移。
作者：Tong Qin, Shaojie Shen(沈劭劼)
机构： 香港科技大学航空机器人
代码：HKUST-Aerial-Robotics/VINS-Mono⭐1345
摘要：Accurate state estimation is a fundamental module for various intelligent applications, such as robot navigation, autonomous driving, virtual and augmented reality. Visual and inertial fusion is a popular technology for 6-DOF state estimation in recent years. Time instants at which different sensors' measurements are recorded are of crucial importance to the system's robustness and accuracy. In practice, timestamps of each sensor typically suffer from triggering and transmission delays, leading to temporal misalignment (time offsets) among different sensors. Such temporal offset dramatically influences the performance of sensor fusion. To this end, we propose an online approach for calibrating temporal offset between visual and inertial measurements. Our approach achieves temporal offset calibration by jointly optimizing time offset, camera and IMU states, as well as feature locations in a SLAM system. Furthermore, the approach is a general model, which can be easily employed in several feature-based optimization frameworks. Simulation and experimental results demonstrate the high accuracy of our calibration approach even compared with other state-of-art offline tools. The VIO comparison against other methods proves that the online temporal calibration significantly benefits visual-inertial systems. The source code of temporal calibration is integrated into our public project, VINS-Mono.
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Virtual to Real Reinforcement Learning for Autonomous Driving,在虚拟环境通过强化学习来训练无人驾驶
作者：Xinlei Pan, Yurong You, Ziyan Wang, Cewu Lu
机构：Berkley、清华大学、上海交通大学
日期：2017-09-26 （2017-04-13 v1）
代码：xinleipan/VirtualtoReal-RL ⭐ 20
摘要：Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first train in a virtual environment and then transfer to the real environment. In this paper, we propose a novel realistic translation network to make model trained in virtual environment be workable in real world. The proposed network can convert non-realistic virtual image input into a realistic one with similar scene structure. Given realistic frames as input, driving policy trained by reinforcement learning can nicely adapt to real world driving. Experiments show that our proposed virtual to real (VR) reinforcement learning (RL) works pretty well. To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data. -
End to End Learning for Self-Driving Cars
作者：Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski 等
机构：英伟达
日期：2016-09-25
代码：marsauto/europilot⭐1237、SullyChen/Autopilot-TensorFlow⭐ 956 非官方
摘要：We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with unclear visual guidance such as in parking lots and on unpaved roads. The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal. We never explicitly trained it to detect, for example, the outline of roads. Compared to explicit decomposition of the problem, such as lane marking detection, path planning, and control, our end-to-end system optimizes all processing steps simultaneously. We argue that this will eventually lead to better performance and smaller systems. Better performance will result because the internal components self-optimize to maximize overall system performance, instead of optimizing human-selected intermediate criteria, e.g., lane detection. Such criteria understandably are selected for ease of human interpretation which doesn't automatically guarantee maximum system performance. Smaller networks are possible because the system learns to solve the problem with the minimal number of processing steps. We used an NVIDIA DevBox and Torch 7 for training and an NVIDIA DRIVE(TM) PX self-driving car computer also running Torch 7 for determining where to drive. The system operates at 30 frames per second (FPS).
- ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst,
:+1: :+1: :+1: :+1: :+1: Waymo出品，通过模仿学习对无人车进行运动规划，全文中文翻译:知乎|每周一篇 & 无人驾驶
作者：Mayank Bansal, Alex Krizhevsky, Abhijit Ogale
机构：Waymo Research
日期:2018-12-07
代码：aidriver/ChauffeurNet 非官方
摘要: Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle. We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough. We propose exposing the learner to synthesized data in the form of perturbations to the expert's driving, which creates interesting situations such as collisions and/or going off the road. Rather than purely imitating all data, we augment the imitation loss with additional losses that penalize undesirable events and encourage progress -- the perturbations then provide an important signal for these losses and lead to robustness of the learned model. We show that the ChauffeurNet model can handle complex situations in simulation, and present ablation experiments that emphasize the importance of each of our proposed changes and show that the model is responding to the appropriate causal factors. Finally, we demonstrate the model driving a car in the real world.