On the Importance of Quantifying Visibility for Autonomous Vehicles under Extreme Precipitation

In the context of autonomous driving, vehicles are inherently bound to encounter more extreme weather during which public safety must be ensured. As climate is quickly changing, the frequency of heavy snowstorms is expected to increase and become a major threat to safe navigation. While there is much literature aiming to improve navigation resiliency to winter conditions, there is a lack of standard metrics to quantify the loss of visibility of lidar sensors related to precipitation. This chapter proposes a novel metric to quantify the lidar visibility loss in real time, relying on the notion of visibility from the meteorology research field. We evaluate this metric on the Canadian Adverse Driving Conditions (CADC) dataset, correlate it with the performance of a state-of-the-art lidar-based localization algorithm, and evaluate the benefit of filtering point clouds before the localization process. We show that the Iterative Closest Point (ICP) algorithm is surprisingly robust against snowfalls, but abrupt events, such as snow gusts, can greatly hinder its accuracy. We discuss such events and demonstrate the need for better datasets focusing on these extreme events to quantify their effect.Comment: Submitted to Intelligent Vehicles and Transportation Volume 3 - De Gruyte

Robust Lidar SLAM Under Adverse Weather

LiDAR Simultaneous Localization and Mapping (SLAM) technologies, which are the foundational technology of autonomous driving, have attracted large interest recently and been a significant research field. The performance of existing State-Of-The-Art LiDAR SLAM systems has been proven to produce accurate odometry estimation on autonomous driving datasets. These datasets are usually collected by vehicles equipped with various sensors under favorable weather conditions. However, challenging weather conditions such as rain and snow are still great obstacles because the rainfalls or snowflakes which are not static will cause noisy points for LiDAR perception and the assumption that the surrounding environment is static will be broken. Specifically, adverse weather will introduce noisy points which have physical structures and could be detected by LiDAR. Meanwhile, these noisy points would tightly surround the LiDAR and block other objects. This will lead to serious deficiencies in environmental structures and introduce more difficulties to pose estimation and loop closure, finally increasing the error of pose estimation and reducing the accuracy of LiDAR SLAM algorithms. Considering that noisy points usually lack the inherent structures exhibited in clean points, we propose a novel denoising framework for point clouds generated from lidar sensors that eliminate stochastic noisy points in a down sampling and super resolution manner to address this issue. Specifically, we first investigate to which degree the performance of the State-Of-The-Art lidar SLAM approaches will decrease when exposed to adverse weather conditions and then implement the denoising framework by combining the DS module with SR module which is based on the U-Net and trained under certain super-solution datasets. The accuracy and robustness of our framework were validated on the Oxford RobotCar dataset and the Canadian Adverse Driving Conditions dataset

A Comparison of Home Care Quality Indicator Rates in Two Canadian Provinces

Background. Home care is becoming an increasingly vital sector in the health care system yet very little is known about the characteristics of home care clients and the quality of care provided in Canada. We describe these clients and evaluate home care quality indicator rates in two regions. Methods. A cross-sectional analysis of assessments completed for older (age 65+) home care clients in both Ontario (n=102,504) and the Winnipeg Regional Health Authority (n=9,250) of Manitoba, using the Resident Assessment Instrument for Home Care (RAI-HC). This assessment has been mandated for use in these two regions and the indicators are generated directly from items within the assessment. The indicators are expressed as rates of negative outcomes (e.g., falls, dehydration). Client-level risk adjustment of the indicator rates was used to enable fair comparisons between the regions. Results. Clients had a mean age of 83.2 years, the majority were female (68.6%) and the regions were very similar on these demographic characteristics. Nearly all clients (92.4%) required full assistance with instrumental activities of daily living (IADLs), approximately 35% had activities of daily living (ADL) impairments, and nearly 50% had some degree of cognitive impairment, which was higher among clients in Ontario (48.8% vs. 37.0%). The highest quality indicator rates were related to clients who had ADL/rehabilitation potential but were not receiving therapy (range: 66.8%-91.6%) and the rate of cognitive decline (65.4%-76.3%). Ontario clients had higher unadjusted rates across 18 of the 22 indicators and the unadjusted differences between the two provinces ranged from 0.6% to 28.4%. For 13 of the 19 indicators that have risk adjustment, after applying the risk adjustment methodology, the difference between the adjusted rates in the two regions was reduced. Conclusions. Home care clients in these two regions are experiencing a significant level of functional and cognitive impairment, health instability and daily pain. The quality indicators provide some important insight into variations between the two regions and can serve as an important decision-support tool for flagging potential quality issues and isolating areas for improvement. Background. Home care is becoming an increasingly vital sector in the health care system yet very little is known about the characteristics of home care clients and the quality of care provided in Canada. We describe these clients and evaluate home care quality indicator rates in two regions. Methods. A cross-sectional analysis of assessments completed for older (age 65+) home care clients in both Ontario (n=102,504) and the Winnipeg Regional Health Authority (n=9,250) of Manitoba, using the Resident Assessment Instrument for Home Care (RAI-HC). This assessment has been mandated for use in these two regions and the indicators are generated directly from items within the assessment. The indicators are expressed as rates of negative outcomes (e.g., falls, dehydration). Client-level risk adjustment of the indicator rates was used to enable fair comparisons between the regions. Results. Clients had a mean age of 83.2 years, the majority were female (68.6%) and the regions were very similar on these demographic characteristics. Nearly all clients (92.4%) required full assistance with instrumental activities of daily living (IADLs), approximately 35% had activities of daily living (ADL) impairments, and nearly 50% had some degree of cognitive impairment, which was higher among clients in Ontario (48.8% vs. 37.0%). The highest quality indicator rates were related to clients who had ADL/rehabilitation potential but were not receiving therapy (range: 66.8%-91.6%) and the rate of cognitive decline (65.4%-76.3%). Ontario clients had higher unadjusted rates across 18 of the 22 indicators and the unadjusted differences between the two provinces ranged from 0.6% to 28.4%. For 13 of the 19 indicators that have risk adjustment, after applying the risk adjustment methodology, the difference between the adjusted rates in the two regions was reduced. Conclusions. Home care clients in these two regions are experiencing a significant level of functional and cognitive impairment, health instability and daily pain. The quality indicators provide some important insight into variations between the two regions and can serve as an important decision-support tool for flagging potential quality issues and isolating areas for improvement

TWICE Dataset: Digital Twin of Test Scenarios in a Controlled Environment

Ensuring the safe and reliable operation of autonomous vehicles under adverse weather remains a significant challenge. To address this, we have developed a comprehensive dataset composed of sensor data acquired in a real test track and reproduced in the laboratory for the same test scenarios. The provided dataset includes camera, radar, LiDAR, inertial measurement unit (IMU), and GPS data recorded under adverse weather conditions (rainy, night-time, and snowy conditions). We recorded test scenarios using objects of interest such as car, cyclist, truck and pedestrian -- some of which are inspired by EURONCAP (European New Car Assessment Programme). The sensor data generated in the laboratory is acquired by the execution of simulation-based tests in hardware-in-the-loop environment with the digital twin of each real test scenario. The dataset contains more than 2 hours of recording, which totals more than 280GB of data. Therefore, it is a valuable resource for researchers in the field of autonomous vehicles to test and improve their algorithms in adverse weather conditions, as well as explore the simulation-to-reality gap. The dataset is available for download at: https://twicedataset.github.io/site/Comment: 8 pages, 13 figures, submitted to IEEE Sensors Journa

Distance, Lending Relationships, and Competition

A recent string of theoretical papers has highlighted the importance of geographical distance in explaining loan rates for small firms.Lenders located in the vicinity of small firms face significantly lower transportation and monitoring costs, and hence wield considerable market power, if competing financiers are located relatively far from the borrowing firms.We study the effect on loan conditions of geographical distance between firms, the lending bank, and all other banks in the vicinity.For our study we employ detailed contract information from more than 15,000 bank loans to small firms comprising the entire loan portfolio of a large Belgian bank.We control for relevant relationship, loan contract, bank branch, firm, and regional characteristics.We report the first comprehensive evidence on the occurrence of spatial price discrimination in bank lending.Loan rates decrease with the distance between the firm and the lending bank and similarly increase with the distance between the firm and competing banks.The effect of distance on the loan rate is statistically significant and economically relevant.Robust to changes in model specifications and variable definitions, the effect is seemingly not driven by the modest changes over time in lending technology that we infer.We deduce that transportation costs cause the spatial price discrimination we observe.prices;credit;banks;competition;bank lending

Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions

Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions. Yet, to achieve the high safety requirement, these perceptual systems must operate robustly under a wide variety of weather conditions including snow and rain. In this paper, we present a new dataset to enable robust autonomous driving via a novel data collection process - data is repeatedly recorded along a 15 km route under diverse scene (urban, highway, rural, campus), weather (snow, rain, sun), time (day/night), and traffic conditions (pedestrians, cyclists and cars). The dataset includes images and point clouds from cameras and LiDAR sensors, along with high-precision GPS/INS to establish correspondence across routes. The dataset includes road and object annotations using amodal masks to capture partial occlusions and 3D bounding boxes. We demonstrate the uniqueness of this dataset by analyzing the performance of baselines in amodal segmentation of road and objects, depth estimation, and 3D object detection. The repeated routes opens new research directions in object discovery, continual learning, and anomaly detection. Link to Ithaca365: https://ithaca365.mae.cornell.edu/Comment: Accepted by CVPR 202

Distance, lending relationships and competition.

Competition;