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"autonomous cars": models, code, and papers

Certified Control: An Architecture for Verifiable Safety of Autonomous Vehicles

Mar 29, 2021
Daniel Jackson, Valerie Richmond, Mike Wang, Jeff Chow, Uriel Guajardo, Soonho Kong, Sergio Campos, Geoffrey Litt, Nikos Arechiga

Widespread adoption of autonomous cars will require greater confidence in their safety than is currently possible. Certified control is a new safety architecture whose goal is two-fold: to achieve a very high level of safety, and to provide a framework for justifiable confidence in that safety. The key idea is a runtime monitor that acts, along with sensor hardware and low-level control and actuators, as a small trusted base, ensuring the safety of the system as a whole. Unfortunately, in current systems complex perception makes the verification even of a runtime monitor challenging. Unlike traditional runtime monitoring, therefore, a certified control monitor does not perform perception and analysis itself. Instead, the main controller assembles evidence that the proposed action is safe into a certificate that is then checked independently by the monitor. This exploits the classic gap between the costs of finding and checking. The controller is assigned the task of finding the certificate, and can thus use the most sophisticated algorithms available (including learning-enabled software); the monitor is assigned only the task of checking, and can thus run quickly and be smaller and formally verifiable. This paper explains the key ideas of certified control and illustrates them with a certificate for LiDAR data and its formal verification. It shows how the architecture dramatically reduces the amount of code to be verified, providing an end-to-end safety analysis that would likely not be achievable in a traditional architecture.

* 18 pages + 15 page Appendix, 11 figures 
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JuncNet: A Deep Neural Network for Road Junction Disambiguation for Autonomous Vehicles

Aug 31, 2018
Saumya Kumaar, Navaneethkrishnan B, Sumedh Mannar, S N Omkar

With a great amount of research going on in the field of autonomous vehicles or self-driving cars, there has been considerable progress in road detection and tracking algorithms. Most of these algorithms use GPS to handle road junctions and its subsequent decisions. However, there are places in the urban environment where it becomes difficult to get GPS fixes which render the junction decision handling erroneous or possibly risky. Vision-based junction detection, however, does not have such problems. This paper proposes a novel deep convolutional neural network architecture for disambiguation of junctions from roads with a high degree of accuracy. This network is benchmarked against other well known classifying network architectures like AlexNet and VGGnet. Further, we discuss a potential road navigation methodology which uses the proposed network model. We conclude by performing an experimental validation of the trained network and the navigational method on the roads of the Indian Institute of Science (IISc).

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FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

Feb 28, 2022
Lidia Fantauzzo, Eros Fani', Debora Caldarola, Antonio Tavera, Fabio Cermelli, Marco Ciccone, Barbara Caputo

Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. However, it operates on sensible data collected from the users' cars; thus, protecting the clients' privacy becomes a primary concern. For similar reasons, Federated Learning has been recently introduced as a new machine learning paradigm aiming to learn a global model while preserving privacy and leveraging data on millions of remote devices. Despite several efforts on this topic, no work has explicitly addressed the challenges of federated learning in semantic segmentation for driving so far. To fill this gap, we propose FedDrive, a new benchmark consisting of three settings and two datasets, incorporating the real-world challenges of statistical heterogeneity and domain generalization. We benchmark state-of-the-art algorithms from the federated learning literature through an in-depth analysis, combining them with style transfer methods to improve their generalization ability. We demonstrate that correctly handling normalization statistics is crucial to deal with the aforementioned challenges. Furthermore, style transfer improves performance when dealing with significant appearance shifts. We plan to make both the code and the benchmark publicly available to the research community.

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Atlas Fusion -- Modern Framework for Autonomous Agent Sensor Data Fusion

Oct 22, 2020
Adam Ligocki, Ales Jelinek, Ludek Zalud

In this paper, we present our new sensor fusion framework for self-driving cars and other autonomous robots. We have designed our framework as a universal and scalable platform for building up a robust 3D model of the agent's surrounding environment by fusing a wide range of various sensors into the data model that we can use as a basement for the decision making and planning algorithms. Our software currently covers the data fusion of the RGB and thermal cameras, 3D LiDARs, 3D IMU, and a GNSS positioning. The framework covers a complete pipeline from data loading, filtering, preprocessing, environment model construction, visualization, and data storage. The architecture allows the community to modify the existing setup or to extend our solution with new ideas. The entire software is fully compatible with ROS (Robotic Operation System), which allows the framework to cooperate with other ROS-based software. The source codes are fully available as an open-source under the MIT license. See

* 8 pages 
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InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling

Jul 16, 2020
Jun Wang, Shiyi Lan, Mingfei Gao, Larry S. Davis

Real-time 3D object detection is crucial for autonomous cars. Achieving promising performance with high efficiency, voxel-based approaches have received considerable attention. However, previous methods model the input space with features extracted from equally divided sub-regions without considering that point cloud is generally non-uniformly distributed over the space. To address this issue, we propose a novel 3D object detection framework with dynamic information modeling. The proposed framework is designed in a coarse-to-fine manner. Coarse predictions are generated in the first stage via a voxel-based region proposal network. We introduce InfoFocus, which improves the coarse detections by adaptively refining features guided by the information of point cloud density. Experiments are conducted on the large-scale nuScenes 3D detection benchmark. Results show that our framework achieves the state-of-the-art performance with 31 FPS and improves our baseline significantly by 9.0% mAP on the nuScenes test set.

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Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars

May 02, 2019
Alexandros Kouris, Stylianos I. Venieris, Michail Rizakis, Christos-Savvas Bouganis

The need to recognise long-term dependencies in sequential data such as video streams has made LSTMs a prominent AI model for many emerging applications. However, the high computational and memory demands of LSTMs introduce challenges in their deployment on latency-critical systems such as self-driving cars which are equipped with limited computational resources on-board. In this paper, we introduce an approximate computing scheme combining model pruning and computation restructuring to obtain a high-accuracy approximation of the result in early stages of the computation. Our experiments demonstrate that using the proposed methodology, mission-critical systems responsible for autonomous navigation and collision avoidance are able to make informed decisions based on approximate calculations within the available time budget, meeting their specifications on safety and robustness.

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A Survey on Safety-Critical Scenario Generation for Autonomous Driving -- A Methodological Perspective

Feb 07, 2022
Wenhao Ding, Chejian Xu, Haohong Lin, Bo Li, Ding Zhao

Autonomous driving systems have witnessed a great development during the past years thanks to the advance in sensing and decision-making. One critical obstacle for their massive deployment in the real world is the evaluation of safety. Most existing driving systems are still trained and evaluated on naturalistic scenarios that account for the vast majority of daily life or heuristically-generated adversarial ones. However, the large population of cars requires an extremely low collision rate, indicating safety-critical scenarios collected in the real world would be rare. Thus, methods to artificially generate artificial scenarios becomes critical to manage the risk and reduce the cost. In this survey, we focus on the algorithms of safety-critical scenario generation. We firstly provide a comprehensive taxonomy of existing algorithms by dividing them into three categories: data-driven generation, adversarial generation, and knowledge-based generation. Then, we discuss useful tools for scenario generation, including simulation platforms and packages. Finally, we extend our discussion to five main challenges of current works -- fidelity, efficiency, diversity, transferability, controllability -- and the research opportunities lighted up by these challenges.

* 16 pages, 4 figures 
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Pedestrian Models for Autonomous Driving Part I: low level models, from sensing to tracking

Feb 26, 2020
Fanta Camara, Nicola Bellotto, Serhan Cosar, Dimitris Nathanael, Matthias Althoff, Jingyuan Wu, Johannes Ruenz, André Dietrich, Charles W. Fox

Autonomous vehicles (AVs) must share space with human pedestrians, both in on-road cases such as cars at pedestrian crossings and off-road cases such as delivery vehicles navigating through crowds on high-streets. Unlike static and kinematic obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detection and tracking which enable such modelling. This narrative review article is Part I of a pair which together survey the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in higher level systems such as behaviour modelling, prediction and interaction control.

* Submitted to IEEE Transactions on Intelligent Transportation Systems 
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Efficient Online Transfer Learning for 3D Object Classification in Autonomous Driving

May 04, 2021
Rui Yang, Zhi Yan, Tao Yang, Yassine Ruichek

Autonomous driving has achieved rapid development over the last few decades, including the machine perception as an important issue of it. Although object detection based on conventional cameras has achieved remarkable results in 2D/3D, non-visual sensors such as 3D LiDAR still have incomparable advantages in the accuracy of object position detection. However, the challenge also exists with the difficulty in properly interpreting point cloud generated by LiDAR. This paper presents a multi-modal-based online learning system for 3D LiDAR-based object classification in urban environments, including cars, cyclists and pedestrians. The proposed system aims to effectively transfer the mature detection capabilities based on visual sensors to the new model learning based on non-visual sensors through a multi-target tracker (i.e. using one sensor to train another). In particular, it integrates the Online Random Forests (ORF) \cite{saffari2009line} method, which inherently has the abilities of fast and multi-class learning. Through experiments, we show that our system is capable of learning a high-performance model for LiDAR-based 3D object classification on-the-fly, which is especially suitable for robotics in-situ deployment while responding to the widespread challenge of insufficient detector generalization capabilities.

* 8 pages, 7 figures, submitted to IEEE ITSC 2021 
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