A Review of Non-Lane Road Marking Detection and Recognition

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

Adam Morrissett, Sherif Abdelwahed

Environment perception is a critical function used by driving automation systems, or self-driving cars, for detecting objects such as obstacles, lane markings, and road signs. In order to replace human drivers, self-driving cars will need to safely operate in parking lots, private roads, underground, or any other environment with poor GPS signals or uncharted infrastructure. While much attention has been spent on recognizing lane markings, non-lane road markings have received considerably less attention. Current perception systems can recognize only a small subset of markings and often only under favorable weather conditions. This limitation is exacerbated by the current quality of scene segmentation data sets. Only a select few of existing data sets have annotations for non-lane road markings, and the ones that do only have them for a small number of marking types. Additionally most of the data sets were generated under one type of driving condition. Finally, it is difficult to determine if current recognition systems can satisfy real-time requirements. This paper investigates the current limitations and challenges for non-lane road marking detection and recognition including recognition capabilities, data set quality, and inference times.

A Physical Testbed for Intelligent Transportation Systems

2019 12th International Conference on Human System Interaction (HSI)

Adam Morrissett, Roja Eini, Mostafa Zaman, Nasibeh Zohrabi, Sherif Abdelwahed

Intelligent transportation systems (ITSs) and other smart-city technologies are increasingly advancing in capability and complexity. While simulation environments continue to improve, their fidelity and ease of use can quickly degrade as newer systems become increasingly complex. To remedy this, we propose a hardware- and software-based traffic management system testbed as part of a larger smart-city testbed. It comprises a network of connected vehicles, a network of intersection controllers, a variety of control services, and data analytics services. The main goal of our testbed is to provide researchers and students with the means to develop novel traffic and vehicle control algorithms with higher fidelity than what can be achieved with simulation alone. Specifically, we are using the testbed to develop an integrated management system that combines model-based control and data analytics to improve the system performance over time. In this paper, we give a detailed description of each component within the testbed and discuss its current developmental state. Additionally, we present initial results and propose future work.

A Physical Testbed for Smart City Research

2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)

Adam Morrissett, Sherif Abdelwahed

City infrastructure is deteriorating, traffic management systems are becoming increasingly inefficient due to volume, and resources are becoming scarce. In the era of information and analytics, the idea of smart cities has been increasingly proposed as a solution to inefficient public services and resource management. While some cities have had success with beginning to transform into smart cities, the process has revealed significant barriers. One of which is the communication infrastructure necessary to create an interconnected network of sensors, actuators, and analytics systems. This barrier is discussed, and a physical testbed for smart city research is proposed. The current progress of the testbed development is reported, and a plan for continued work is outlined.