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"autonomous cars": models, code, and papers

Explainable Goal-Driven Agents and Robots- A Comprehensive Review and New Framework

Apr 21, 2020
Fatai Sado, Chu Kiong Loo, Matthias Kerzel, Stefan Wermter

Recent applications of autonomous agents and robots, for example, self-driving cars, scenario-based trainers, exploration robots, service robots, have brought attention to crucial trust-related problems associated with the current generation of artificial intelligence (AI) systems. AI systems particularly dominated by the connectionist deep learning neural network approach lack capabilities of explaining their decisions and actions to others, despite their great successes. They are fundamentally non-intuitive black boxes, which renders their decision or actions opaque, making it difficult to trust them in safety-critical applications. The recent stance on the explainability of AI systems has witnessed several works on eXplainable Artificial Intelligence; however, most of the studies have focused on data-driven XAI systems applied in computational sciences. Studies addressing the increasingly pervasive goal-driven agents and robots are still missing. This paper reviews works on explainable goal-driven intelligent agents and robots, focusing on techniques for explaining and communicating agents perceptual functions (for example, senses, vision, etc.) and cognitive reasoning (for example, beliefs, desires, intention, plans, and goals) with humans in the loop. The review highlights key strategies that emphasize transparency and understandability, and continual learning for explainability. Finally, the paper presents requirements for explainability and suggests a roadmap for the possible realization of effective goal-driven explainable agents and robots


Input Prioritization for Testing Neural Networks

Jan 11, 2019
Taejoon Byun, Vaibhav Sharma, Abhishek Vijayakumar, Sanjai Rayadurgam, Darren Cofer

Deep neural networks (DNNs) are increasingly being adopted for sensing and control functions in a variety of safety and mission-critical systems such as self-driving cars, autonomous air vehicles, medical diagnostics, and industrial robotics. Failures of such systems can lead to loss of life or property, which necessitates stringent verification and validation for providing high assurance. Though formal verification approaches are being investigated, testing remains the primary technique for assessing the dependability of such systems. Due to the nature of the tasks handled by DNNs, the cost of obtaining test oracle data---the expected output, a.k.a. label, for a given input---is high, which significantly impacts the amount and quality of testing that can be performed. Thus, prioritizing input data for testing DNNs in meaningful ways to reduce the cost of labeling can go a long way in increasing testing efficacy. This paper proposes using gauges of the DNN's sentiment derived from the computation performed by the model, as a means to identify inputs that are likely to reveal weaknesses. We empirically assessed the efficacy of three such sentiment measures for prioritization---confidence, uncertainty, and surprise---and compare their effectiveness in terms of their fault-revealing capability and retraining effectiveness. The results indicate that sentiment measures can effectively flag inputs that expose unacceptable DNN behavior. For MNIST models, the average percentage of inputs correctly flagged ranged from 88% to 94.8%.


Predictive Collision Management for Time and Risk Dependent Path Planning

Nov 26, 2020
Carsten Hahn, Sebastian Feld, Hannes Schroter

Autonomous agents such as self-driving cars or parcel robots need to recognize and avoid possible collisions with obstacles in order to move successfully in their environment. Humans, however, have learned to predict movements intuitively and to avoid obstacles in a forward-looking way. The task of collision avoidance can be divided into a global and a local level. Regarding the global level, we propose an approach called "Predictive Collision Management Path Planning" (PCMP). At the local level, solutions for collision avoidance are used that prevent an inevitable collision. Therefore, the aim of PCMP is to avoid unnecessary local collision scenarios using predictive collision management. PCMP is a graph-based algorithm with a focus on the time dimension consisting of three parts: (1) movement prediction, (2) integration of movement prediction into a time-dependent graph, and (3) time and risk-dependent path planning. The algorithm combines the search for a shortest path with the question: is the detour worth avoiding a possible collision scenario? We evaluate the evasion behavior in different simulation scenarios and the results show that a risk-sensitive agent can avoid 47.3% of the collision scenarios while making a detour of 1.3%. A risk-averse agent avoids up to 97.3% of the collision scenarios with a detour of 39.1%. Thus, an agent's evasive behavior can be controlled actively and risk-dependent using PCMP.

* Proceedings of the 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL '20), 2020, Pages 405-408 
* Extended version of the SIGSPATIAL '20 paper 

Localization Uncertainty Estimation for Anchor-Free Object Detection

Jun 28, 2020
Youngwan Lee, Joong-won Hwang, Hyung-Il Kim, Kimin Yun, Joungyoul Park

Since many safety-critical systems such as surgical robots and autonomous driving cars are in unstable environments with sensor noise or incomplete data, it is desirable for object detectors to take the confidence of the localization prediction into account. Recent attempts to estimate localization uncertainty for object detection focus only anchor-based method that captures the uncertainty of different characteristics such as location (center point) and scale (width, height). Also, anchor-based methods need to adjust sensitive anchor-box settings. Therefore, we propose a new object detector called Gaussian-FCOS that estimates the localization uncertainty based on an anchor-free detector that captures the uncertainty of similar property with four directions of box offsets (left, right, top, bottom) and avoids the anchor tuning. For this purpose, we design a new loss function, uncertainty loss, to measure how uncertain the estimated object location is by modeling the uncertainty as a Gaussian distribution. Then, the detection score is calibrated through the estimated uncertainty. Experiments on challenging COCO datasets demonstrate that the proposed new loss function not only enables the network to estimate the uncertainty but produces a synergy effect with regression loss. In addition, our Gaussian-FCOS reduces false positives with the estimated localization uncertainty and finds more missing-objects, boosting both Average Precision (AP) and Recall (AR). We hope Gaussian-FCOS serve as a baseline for the reliability-required task.


Foresee: Attentive Future Projections of Chaotic Road Environments with Online Training

May 30, 2018
Anil Sharma, Prabhat Kumar

In this paper, we train a recurrent neural network to learn dynamics of a chaotic road environment and to project the future of the environment on an image. Future projection can be used to anticipate an unseen environment for example, in autonomous driving. Road environment is highly dynamic and complex due to the interaction among traffic participants such as vehicles and pedestrians. Even in this complex environment, a human driver is efficacious to safely drive on chaotic roads irrespective of the number of traffic participants. The proliferation of deep learning research has shown the efficacy of neural networks in learning this human behavior. In the same direction, we investigate recurrent neural networks to understand the chaotic road environment which is shared by pedestrians, vehicles (cars, trucks, bicycles etc.), and sometimes animals as well. We propose \emph{Foresee}, a unidirectional gated recurrent units (GRUs) network with attention to project future of the environment in the form of images. We have collected several videos on Delhi roads consisting of various traffic participants, background and infrastructure differences (like 3D pedestrian crossing) at various times on various days. We train \emph{Foresee} in an unsupervised way and we use online training to project frames up to $0.5$ seconds in advance. We show that our proposed model performs better than state of the art methods (prednet and Enc. Dec. LSTM) and finally, we show that our trained model generalizes to a public dataset for future projections.


A Nonlinear Constrained Optimization Framework for Comfortable and Customizable Motion Planning of Nonholonomic Mobile Robots - Part I

May 22, 2013
Shilpa Gulati, Chetan Jhurani, Benjamin Kuipers

In this series of papers, we present a motion planning framework for planning comfortable and customizable motion of nonholonomic mobile robots such as intelligent wheelchairs and autonomous cars. In this first one we present the mathematical foundation of our framework. The motion of a mobile robot that transports a human should be comfortable and customizable. We identify several properties that a trajectory must have for comfort. We model motion discomfort as a weighted cost functional and define comfortable motion planning as a nonlinear constrained optimization problem of computing trajectories that minimize this discomfort given the appropriate boundary conditions and constraints. The optimization problem is infinite-dimensional and we discretize it using conforming finite elements. We also outline a method by which different users may customize the motion to achieve personal comfort. There exists significant past work in kinodynamic motion planning, to the best of our knowledge, our work is the first comprehensive formulation of kinodynamic motion planning for a nonholonomic mobile robot as a nonlinear optimization problem that includes all of the following - a careful analysis of boundary conditions, continuity requirements on trajectory, dynamic constraints, obstacle avoidance constraints, and a robust numerical implementation. In this paper, we present the mathematical foundation of the motion planning framework and formulate the full nonlinear constrained optimization problem. We describe, in brief, the discretization method using finite elements and the process of computing initial guesses for the optimization problem. Details of the above two are presented in Part II of the series.


Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection

Jul 14, 2021
Velat Kilic, Deepti Hegde, Vishwanath Sindagi, A. Brinton Cooper, Mark A. Foster, Vishal M. Patel

Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. However, they are known to be sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR). As a result, lidar-based object detectors trained on data captured in normal weather tend to perform poorly in such scenarios. However, collecting and labelling sufficient training data in a diverse range of adverse weather conditions is laborious and prohibitively expensive. To address this issue, we propose a physics-based approach to simulate lidar point clouds of scenes in adverse weather conditions. These augmented datasets can then be used to train lidar-based detectors to improve their all-weather reliability. Specifically, we introduce a hybrid Monte-Carlo based approach that treats (i) the effects of large particles by placing them randomly and comparing their back reflected power against the target, and (ii) attenuation effects on average through calculation of scattering efficiencies from the Mie theory and particle size distributions. Retraining networks with this augmented data improves mean average precision evaluated on real world rainy scenes and we observe greater improvement in performance with our model relative to existing models from the literature. Furthermore, we evaluate recent state-of-the-art detectors on the simulated weather conditions and present an in-depth analysis of their performance.


Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations

Aug 13, 2020
Elena Arcari, Andrea Carron, Melanie N. Zeilinger

Data availability has dramatically increased in recent years, driving model-based control methods to exploit learning techniques for improving the system description, and thus control performance. Two key factors that hinder the practical applicability of learning methods in control are their high computational complexity and limited generalization capabilities to unseen conditions. Meta-learning is a powerful tool that enables efficient learning across a finite set of related tasks, easing adaptation to new unseen tasks. This paper makes use of a meta-learning approach for adaptive model predictive control, by learning a system model that leverages data from previous related tasks, while enabling fast fine-tuning to the current task during closed-loop operation. The dynamics is modeled via Gaussian process regression and, building on the Karhunen-Lo{\`e}ve expansion, can be approximately reformulated as a finite linear combination of kernel eigenfunctions. Using data collected over a set of tasks, the eigenfunction hyperparameters are optimized in a meta-training phase by maximizing a variational bound for the log-marginal likelihood. During meta-testing, the eigenfunctions are fixed, so that only the linear parameters are adapted to the new unseen task in an online adaptive fashion via Bayesian linear regression, providing a simple and efficient inference scheme. Simulation results are provided for autonomous racing with miniature race cars adapting to unseen road conditions.


Complex-YOLO: Real-time 3D Object Detection on Point Clouds

Sep 24, 2018
Martin Simon, Stefan Milz, Karl Amende, Horst-Michael Gross

Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and motion planning. The capacity of inferencing highly sparse 3D data in real-time is an ill-posed problem for lots of other application areas besides automated vehicles, e.g. augmented reality, personal robotics or industrial automation. We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only. In this work, we describe a network that expands YOLOv2, a fast 2D standard object detector for RGB images, by a specific complex regression strategy to estimate multi-class 3D boxes in Cartesian space. Thus, we propose a specific Euler-Region-Proposal Network (E-RPN) to estimate the pose of the object by adding an imaginary and a real fraction to the regression network. This ends up in a closed complex space and avoids singularities, which occur by single angle estimations. The E-RPN supports to generalize well during training. Our experiments on the KITTI benchmark suite show that we outperform current leading methods for 3D object detection specifically in terms of efficiency. We achieve state of the art results for cars, pedestrians and cyclists by being more than five times faster than the fastest competitor. Further, our model is capable of estimating all eight KITTI-classes, including Vans, Trucks or sitting pedestrians simultaneously with high accuracy.


Exploring the Effects of Data Augmentation for Drivable Area Segmentation

Aug 06, 2022
Srinjoy Bhuiya, Ayushman Kumar, Sankalok Sen

The real-time segmentation of drivable areas plays a vital role in accomplishing autonomous perception in cars. Recently there have been some rapid strides in the development of image segmentation models using deep learning. However, most of the advancements have been made in model architecture design. In solving any supervised deep learning problem related to segmentation, the success of the model that one builds depends upon the amount and quality of input training data we use for that model. This data should contain well-annotated varied images for better working of the segmentation model. Issues like this pertaining to annotations in a dataset can lead the model to conclude with overwhelming Type I and II errors in testing and validation, causing malicious issues when trying to tackle real world problems. To address this problem and to make our model more accurate, dynamic, and robust, data augmentation comes into usage as it helps in expanding our sample training data and making it better and more diversified overall. Hence, in our study, we focus on investigating the benefits of data augmentation by analyzing pre-existing image datasets and performing augmentations accordingly. Our results show that the performance and robustness of existing state of the art (or SOTA) models can be increased dramatically without any increase in model complexity or inference time. The augmentations decided on and used in this paper were decided only after thorough research of several other augmentation methodologies and strategies and their corresponding effects that are in widespread usage today. All our results are being reported on the widely used Cityscapes Dataset.

* 16 pages, 10 figures, Presented at International Conference of Undergraduate Students, ICUS 2021