Landslide monitoring by fixed-base terrestrial stereo-photogrammetry

Photogrammetry has been used since long to periodically control the evolution of landslides; however, true monitoring is reserved to robotic total stations and ground based InSAR systems, capable of high frequency, high accurate 24h/day response. This paper presents the first results of a fixed terrestrial stereo photogrammetric system developed to monitor shape changes of the scene. The system is made of two reflex cameras, each contained in a sealed box with a control computer that periodically acquires an image and send it to a host computer; once an image pair is received from the two cameras, the DSM of the scene is generated by image correlation and made available for archiving or analysis. The system has been installed and is being tested on the Mont de la Saxe landslide, where several monitoring system are active. Some instability of the camera attitude has been noticed and is corrected with an automated procedure. First comparisons with InSAR data show a good agreement

Insights into temperature controls on rockfall occurrence and cliff erosion

A variety of environmental triggers have been associated with the occurrence of rockfalls however their role and relative significance remains poorly constrained. This is in part due to the lack of concurrent data on rockfall occurrence and cliff face conditions at temporal resolutions that mirror the variability of environmental conditions, and over durations for large enough numbers of rockfall events to be captured. The aim of this thesis is to fill this data gap, and then to specifically focus on the role of temperature in triggering rockfall that this data illuminates. To achieve this, a long-term multiannual 3D rockfall dataset and contemporaneous Infrared Thermography (IRT) monitoring of cliff surface temperatures has been generated. The approaches used in this thesis are undertaken at East Cliff, Whitby, which is a coastal cliff located in North Yorkshire, UK. The monitored section is ~ 200 m wide and ~65 m high, with a total cliff face area of ~9,592 m². A method for the automated quantification of rockfall volumes is used to explore data collected between 2017–2019 and 2021, with the resulting inventory including > 8,300 rockfalls from 2017–2019 and > 4,100 rockfalls in 2021, totalling > 12,400 number of rockfalls. The analysis of the inventory demonstrates that during dry conditions, increases in rockfall frequency are coincident with diurnal surface temperature fluctuations, notably at sunrise, noon and sunset in all seasons, leading to a marked diurnal pattern of rockfall. Statistically significant relationships are observed to link cliff temperature and rockfall, highlighting the response of rock slopes to absolute temperatures and changes in temperature. This research also shows that inclement weather constitutes the dominant control over the annual production of rockfalls but also quantifies the period when temperature controls are dominant. Temperature-controlled rockfall activity is shown to have an important erosional role, particularly in periods of iterative erosion dominated by small size rockfalls. As such, this thesis provides for the first high-resolution evidence of temperature controls on rockfall activity, cliff erosion and landform development

Forward Scattering Meter for Visibility Measurements

Atmospheric aerosols, containing water, constitute most of the air during non-ideal weather conditions including fog, haze, and mist. These aerosols cause light to be attenuated while propagating through the atmosphere causing the effective visibility to decrease. The visibility is dependent on the extinction coefficient of the aerosol distribution that can be found using Mie scattering theory. In the case of a real environment a distribution of particle sizes must be considered where the particles present are described by a weighted value relative to the number density. In this thesis a forward scattering meter is devised that measures the amount of scattered light at a specific forward scattering angle under the assumption that the scattered light is linearly related to the extinction coefficient of different weather conditions. To validate the design, it will be compared against a commercial visibility meter along with using a fog chamber to simulate various weather conditions

Can Automated Vehicles "See" in Minnesota? Ambient Particle Effects on LiDAR

(c)1035427This project will use a combination of laboratory experimentation and road demonstrations to better understand the reduction of LiDAR signal and object detection capability under adverse weather conditions found in Minnesota. It will also lead to concepts to improve LiDAR systems to adapt to such conditions through better signal processing image recognition software

Advisory Safety System for Autonomous Vehicles under Sun-glare

Autonomous Vehicles (AVs) are expected to provide a large number of benefits such as improving comfort, vehicle safety and traffic flow. AVs use various sensors and control systems to empower driver’s decision-making under uncertainties as well as, assist the driving task under adverse conditions such as vision impairment. Excessive sunlight has been recognized as the primary source of the reduction in vision performance during daytime. Sun glare oftentimes leads to an impaired visibility for drivers and has been studied from different aspects on roadways. However, there is a lack of knowledge regarding the potential detrimental effects of natural light brightness differential, particularly sun glare on driving behavior and its possible risks. This dissertation addresses this issue by developing an integrated vehicle safety methodology as an advisory system for safe driving under sun glare. The main contribution of this research is to establish a real-time detection of the vision impairment area on roadways. This study also proposes a Collision Avoidance System Under Sun-glare (CASUS) in which upcoming possible vision impairment is detected, a warning message is sent, and the speed of vehicle is adjusted accordingly. In this context, real-world data is used to calibrate a psychophysical car-following model within VISSIM, a traffic microscopic simulation tool. Traffic safety impacts are explored through the number of conflicts extracted from the microsimulation tool and assessed by the time-to-collision indicator. Conventional/human-driven vehicles and different type of AVs are modeled for a straight segment of the TransCanada highway under various AVs penetration rates. The findings revealed a significant reduction in potential collisions due to adjustment of travel speed of AVs under the sun glare. The results also indicated that applying CASUS to the AVs with a failing sensory system improves traffic safety by providing optimal-safe speeds. Furthermore, the CASUS algorithm has the potential to be integrated into driving simulators or real vehicles to further evaluate and examine its benefits under different vision impairment scenarios