An evaluation framework for stereo-based driver assistance

This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury database. However, equivalent data for automotive or robotics applications rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases. Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on pixel and object level. In more detail we evaluate an intermediate representation called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km

Improving Traffic Safety Through Video Analysis in Jakarta, Indonesia

This project presents the results of a partnership between the Data Science for Social Good fellowship, Jakarta Smart City and Pulse Lab Jakarta to create a video analysis pipeline for the purpose of improving traffic safety in Jakarta. The pipeline transforms raw traffic video footage into databases that are ready to be used for traffic analysis. By analyzing these patterns, the city of Jakarta will better understand how human behavior and built infrastructure contribute to traffic challenges and safety risks. The results of this work should also be broadly applicable to smart city initiatives around the globe as they improve urban planning and sustainability through data science approaches.Comment: 6 pages; LaTeX; Presented at NeurIPS 2018 Workshop on Machine Learning for the Developing World; Presented at NeurIPS 2018 Workshop on AI for Social Goo

Mapping and classification of ecologically sensitive marine habitats using unmanned aerial vehicle (UAV) imagery and object-based image analysis (OBIA)

Nowadays, emerging technologies, such as long-range transmitters, increasingly miniaturized components for positioning, and enhanced imaging sensors, have led to an upsurge in the availability of new ecological applications for remote sensing based on unmanned aerial vehicles (UAVs), sometimes referred to as “drones”. In fact, structure-from-motion (SfM) photogrammetry coupled with imagery acquired by UAVs offers a rapid and inexpensive tool to produce high-resolution orthomosaics, giving ecologists a new way for responsive, timely, and cost-effective monitoring of ecological processes. Here, we adopted a lightweight quadcopter as an aerial survey tool and object-based image analysis (OBIA) workflow to demonstrate the strength of such methods in producing very high spatial resolution maps of sensitive marine habitats. Therefore, three different coastal environments were mapped using the autonomous flight capability of a lightweight UAV equipped with a fully stabilized consumer-grade RGB digital camera. In particular we investigated a Posidonia oceanica seagrass meadow, a rocky coast with nurseries for juvenile fish, and two sandy areas showing biogenic reefs of Sabelleria alveolata. We adopted, for the first time, UAV-based raster thematic maps of these key coastal habitats, produced after OBIA classification, as a new method for fine-scale, low-cost, and time saving characterization of sensitive marine environments which may lead to a more effective and efficient monitoring and management of natural resource

Information flow and cooperative control of vehicle formations

We consider the problem of cooperation among a collection of vehicles performing a shared task using intervehicle communication to coordinate their actions. Tools from algebraic graph theory prove useful in modeling the communication network and relating its topology to formation stability. We prove a Nyquist criterion that uses the eigenvalues of the graph Laplacian matrix to determine the effect of the communication topology on formation stability. We also propose a method for decentralized information exchange between vehicles. This approach realizes a dynamical system that supplies each vehicle with a common reference to be used for cooperative motion. We prove a separation principle that decomposes formation stability into two components: Stability of this is achieved information flow for the given graph and stability of an individual vehicle for the given controller. The information flow can thus be rendered highly robust to changes in the graph, enabling tight formation control despite limitations in intervehicle communication capability

Understanding a Dynamic World: Dynamic Motion Estimation for Autonomous Driving Using LIDAR

In a society that is heavily reliant on personal transportation, autonomous vehicles present an increasingly intriguing technology. They have the potential to save lives, promote efficiency, and enable mobility. However, before this vision becomes a reality, there are a number of challenges that must be solved. One key challenge involves problems in dynamic motion estimation, as it is critical for an autonomous vehicle to have an understanding of the dynamics in its environment for it to operate safely on the road. Accordingly, this thesis presents several algorithms for dynamic motion estimation for autonomous vehicles. We focus on methods using light detection and ranging (LIDAR), a prevalent sensing modality used by autonomous vehicle platforms, due to its advantages over other sensors, such as cameras, including lighting invariance and fidelity of 3D geometric data. First, we propose a dynamic object tracking algorithm. The proposed method takes as input a stream of LIDAR data from a moving object collected by a multi-sensor platform. It generates an estimate of its trajectory over time and a point cloud model of its shape. We formulate the problem similarly to simultaneous localization and mapping (SLAM), allowing us to leverage existing techniques. Unlike prior work, we properly handle a stream of sensor measurements observed over time by deriving our algorithm using a continuous-time estimation framework. We evaluate our proposed method on a real-world dataset that we collect. Second, we present a method for scene flow estimation from a stream of LIDAR data. Inspired by optical flow and scene flow from the computer vision community, our framework can estimate dynamic motion in the scene without relying on segmentation and data association while still rivaling the results of state-of-the-art object tracking methods. We design our algorithms to exploit a graphics processing unit (GPU), enabling real-time performance. Third, we leverage deep learning tools to build a feature learning framework that allows us to train an encoding network to estimate features from a LIDAR occupancy grid. The learned feature space describes the geometric and semantic structure of any location observed by the LIDAR data. We formulate the training process so that distances in this learned feature space are meaningful in comparing the similarity of different locations. Accordingly, we demonstrate that using this feature space improves our estimate of the dynamic motion in the environment over time. In summary, this thesis presents three methods to aid in understanding a dynamic world for autonomous vehicle applications with LIDAR. These methods include a novel object tracking algorithm, a real-time scene flow estimation method, and a feature learning framework to aid in dynamic motion estimation. Furthermore, we demonstrate the performance of all our proposed methods on a collection of real-world datasets.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147587/1/aushani_1.pd