Modar Horani

Lane line detection is a very critical element for both Advanced Driver Assistance Systems (ADAS) and Autonomous Driving features. Although, there has been significant amount of research dedicated to the detection and localization of lane lines in the past decade, there is still a gap in the robustness of the implemented systems. A major challenge to the existing lane line detection algorithms stems from coping with bad weather conditions (eg rain, snow, fog, haze, etc.). Snow offers an… Show more

Lane line detection is a very critical element for both Advanced Driver Assistance Systems (ADAS) and Autonomous Driving features. Although, there has been significant amount of research dedicated to the detection and localization of lane lines in the past decade, there is still a gap in the robustness of the implemented systems. A major challenge to the existing lane line detection algorithms stems from coping with bad weather conditions (eg rain, snow, fog, haze, etc.). Snow offers an especially challenging environment, where lane marks and road boundaries are completely covered by snow. In these scenarios, on-board sensors such as cameras, LiDAR, and radars are of very limited benefit. In this research, the focus is on solving the problem of improving robustness of lane line detection in adverse weather conditions, especially snow. A framework is proposed that relies on utilizing Vehicle-to-Infrastructure (V2I … Show less

Lane line detection is a very critical element for Advanced Driver Assistance Systems (ADAS). Although, there has been significant amount of research dedicated to the detection and localization of lane lines in the past decade, there is still a gap in the robustness of the implemented systems. A major challenge to the existing lane line detection algorithms stems from coping with bad weather conditions (eg rain, snow, fog, haze, etc.). Snow offers an especially challenging environment, where… Show more

Lane line detection is a very critical element for Advanced Driver Assistance Systems (ADAS). Although, there has been significant amount of research dedicated to the detection and localization of lane lines in the past decade, there is still a gap in the robustness of the implemented systems. A major challenge to the existing lane line detection algorithms stems from coping with bad weather conditions (eg rain, snow, fog, haze, etc.). Snow offers an especially challenging environment, where lane marks and road boundaries are completely covered by snow. In these scenarios, on-board sensors such as cameras, LiDAR, and radars are of very limited benefit. In this research, the focus is on solving the problem of improving robustness of lane line detection in adverse weather conditions, especially snow. A framework is proposed that relies on using Vehicle-to-Infrastructure (V2I) communication to access reference … Show less

Current implementations of vision-based Advanced Driver Assistance Systems (ADAS) are largely dependent on real-time vehicle camera data along with other sensory data available on-board such as radar, ultrasonic, and GPS data. This data, when accurately reported and processed, helps the vehicle avoid collisions using established ADAS applications such as Forward Collision Avoidance (FCA), Autonomous Cruise Control (ACC), Pedestrian Detection, etc. Vehicle to Vehicle (V2V) and Vehicle to… Show more

Current implementations of vision-based Advanced Driver Assistance Systems (ADAS) are largely dependent on real-time vehicle camera data along with other sensory data available on-board such as radar, ultrasonic, and GPS data. This data, when accurately reported and processed, helps the vehicle avoid collisions using established ADAS applications such as Forward Collision Avoidance (FCA), Autonomous Cruise Control (ACC), Pedestrian Detection, etc. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) over Dedicated Short Range Communication (DSRC) provides basic sensory data from other vehicles or roadside infrastructure including position information of surrounding traffic. Exchanging rich data such as vision data between multiple vehicles, and between vehicles and infrastructure provides a unique opportunity to advance driver assistance applications and Intelligent Transportation Systems (ITS). A primary example is to receive vision data from the vehicle ahead while approaching a busy intersection and then to use this as a priori data in a pedestrian detection algorithm to reach decisions with higher degree of confidence when the vehicle arrives at the intersection. While the possibility of improving ADAS applications utilizing V2V and V2I seems obvious, it is still currently unclear as to what extent. This paper explores the potential for utilizing V2V and V2I communication concepts to advance vision-based ADAS. Three use cases are discussed in terms of feasibility and viability. Show less

Traditional Heat Ventilation and Air Conditioning (HVAC) control systems are reactive by design and largely dependent on the on-board sensory data available on a Controller Area Network (CAN) bus. The increasingly common Internet connectivity offered in today's vehicles, through infotainment and telematic systems, makes data available that may be used to improve current HVAC systems. This includes real-time outside relative humidity, ambient temperature, precipitation (ie, rain, snow, etc.)… Show more

Traditional Heat Ventilation and Air Conditioning (HVAC) control systems are reactive by design and largely dependent on the on-board sensory data available on a Controller Area Network (CAN) bus. The increasingly common Internet connectivity offered in today's vehicles, through infotainment and telematic systems, makes data available that may be used to improve current HVAC systems. This includes real-time outside relative humidity, ambient temperature, precipitation (ie, rain, snow, etc.), and weather forecasts. This data, combined with position and route information of the vehicle, may be used to provide a more comfortable experience to vehicle occupants in addition to improving driver visibility through more intelligent humidity, and defrost control. While the possibility of improving HVAC control utilizing internet connectivity seems obvious, it is still currently unclear as to what extent. In the process of identifying such use cases, a study is conducted to understand the current weather data available by various internet service providers. Hence, this paper focuses on investigating potential use cases for utilizing internet data connectivity in an automotive HVAC module. The considered use cases are then evaluated in terms of feasibility and viability from the perspective of consumer, car manufacturer, and technological complexity. The prototyping of some of the more viable ideas is also examined, along with the defining of an initial test plan to measure the performance of the prototype system. Show less