Parking Camera Calibration for Assisting Automated Road Defect Detection

This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Osaka University.Accurate and timely information is essential for efficient road maintenance planning. Current practice mainly depends on manual visual surveys that are laborious, time consuming, subjective and not frequent enough. We overcame this limitation in our previous work, by proposing a method that automatically detects road defects in video frames collected by a parking camera. The use of such a camera leads to capturing the surroundings of the road, such as sidewalks and sky due to its wide field of view. This unnecessarily reduces the method’s performance. This paper presents a process that identifies the correct Region of Interest (myROI). myROI corresponds to the region of the camera’s field of view that corresponds to the road lane, while considering defect inspection guidelines. We use the theory of inverse perspective mapping (IPM) to map the road frame coordinates to world coordinates. The camera specifications, and position, lane width and road defect detection guidelines constitute the parking camera calibration parameters for the calculation of myROI’s span and boundaries. We performed computational experiments in MATLAB to calculate myROI, and validated the results with field experiments, where we used a metric tape to measure the road defects. Preliminary results show that the proposed process is capable of calculating myROI.This material is based in part upon work supported by the National Science Foundation under Grant Number 1031329

Panorama-Based Multilane Recognition for Advanced Navigation Map Generation

Precise navigation map is crucial in many fields. This paper proposes a panorama based method to detect and recognize lane markings and traffic signs on the road surface. Firstly, to deal with the limited field of view and the occlusion problem, this paper designs a vision-based sensing system which consists of a surround view system and a panoramic system. Secondly, in order to detect and identify traffic signs on the road surface, sliding window based detection method is proposed. Template matching method and SVM (Support Vector Machine) are used to recognize the traffic signs. Thirdly, to avoid the occlusion problem, this paper utilities vision based ego-motion estimation to detect and remove other vehicles. As surround view images contain less dynamic information and gray scales, improved ICP (Iterative Closest Point) algorithm is introduced to ensure that the ego-motion parameters are consequently obtained. For panoramic images, optical flow algorithm is used. The results from the surround view system help to filter the optical flow and optimize the ego-motion parameters; other vehicles are detected by the optical flow feature. Experimental results show that it can handle different kinds of lane markings and traffic signs well

Intelligent Vehicle Development through Scalable Data Collection Processes and Simulation

With current automotive trends in both vehicle electrification and intelligent features such as Advanced Driver-Assistance Systems (ADAS), there is a significant need for a modern vehicle development process which makes use of big data. In the following report, a scalable, phone-based, driving data collection system is developed and applied to powertrain design through a motivating example. Initial project efforts are directed towards the development of both a data collection platform and a system which is capable of interpreting and storing the collected drive data. The developed UWAFT Innovation Platform (UIP) and Monocular Vision Pipeline (MVP) are a functional system which attempt to precipitate crowdsourcing of data collection through a low system cost and open software approach. In an application of this platform data is collected by a test driver for a month in the form of a pilot project, with results evaluated in terms of geographical coverage and with the development of a statistical event profile detailing events of simulation value. The data collected contains over 6 million data points, and over 7.45hrs of driving. In evaluating MVP performance, the You Only Look Once (YOLO) multi-object detector and Markov Decision Process (MDP) multi-object tracker are implemented, with results demonstrating robustness to occlusions and the capability to detect far-away pedestrians and vehicles. With this data collection system functional, and the data from the pilot project experiment, a powertrain simulation environment for University of Waterloo Alternative Fuels Team (UWAFT) is developed. Given the Advanced Vehicle Technology Competition (AVTC) process, it is crucial to continue to explore and design novel powertrain configurations in an environment which is conducive to flexible configuration and with acceptable ease-of-use. Of the environments available, Simscape is selected and a novel Metal-Air Extended Range Electric Vehicle (MA-EREV) powertrain model is developed as a validation of the simulation tool. Upon validating simulated VTS against existing work, results are consistent excluding a 15% reduction in estimated range and a 41% decrease in 50-70 mph acceleration time. To provide an example of the data-driven approach, a winter-driving scenario where the pilot project driver demonstrated slipping is imported as a drive cycle in the MA-EREV model and simulated in an experiment. In analyzing results traction performance of the MA-EREV is evaluated. The MA-EREV weighs 677kg more than the pilot project vehicle, and has increased starting torque due to electrification. In analyzing the results of this scenario replication, the longitudinal slip on the tires reached a maximum of 41% slip (94% of available traction) during stopping and 84% slip (55% of available traction) during acceleration from stop, with more slipping overall during acceleration than stopping. This result indicates that the MA-EREV may need additional traction considerations for safe performance in winter conditions

Driver lane change intention inference using machine learning methods.

Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.PhD in Transpor