Joint Chilean and US mobility testing in extreme environments

Vehicle mobility in cold and challenging terrains is of interest to both the US and Chilean Armies. Mobility in winter conditions is highly vehicle dependent with autonomous vehicles experiencing additional challenges over manned vehicles. They lack the ability to make informed decisions based on what they are “seeing” and instead need to rely on input from sensors on the vehicle, or from Unmanned Aerial Systems (UAS) or satellite data collections. This work focuses on onboard vehicle Controller Area Network (CAN) Bus sensors, driver input sensors, and some externally mounted sensors to assist with terrain identification and overall vehicle mobility. Analysis of winter vehicle/sensor data collected in collaboration with the Chilean Army in Lonquimay, Chile during July and August 2019 will be discussed in this report.

Performance of Mobile LiDAR in Real Road Driving Conditions

The performance of LiDAR sensors deteriorates under adverse weather conditions such as rainfall. However, few studies have empirically analyzed this phenomenon. Hence, we investigated differences in sensor data due to environmental changes (distance from objects (road signs), object material, vehicle (sensor) speed, and amount of rainfall) during LiDAR sensing of road facilities. The indicators used to verify the performance of LiDAR were numbers of point cloud (NPC) and intensity. Differences in the indicators were tested through a two-way ANOVA. First, both NPC and intensity increased with decreasing distance. Second, despite some exceptions, changes in speed did not affect the indicators. Third, the values of NPC do not differ depending on the materials and the intensity of each material followed the order aluminum > steel > plastic > wood, although exceptions were found. Fourth, with an increase in rainfall, both indicators decreased for all materials; specifically, under rainfall of 40 mm/h or more, a substantial reduction was observed. These results demonstrate that LiDAR must overcome the challenges posed by inclement weather to be applicable in the production of road facilities that improve the effectiveness of autonomous driving sensors.

Smart Vehicle Sensor System

Over recent years production of the vehicle around the world has increased rapidly, vehicle theft has become a shared concern for all citizens. Security and safety have always become a necessity. However, present anti-theft systems lack the tracking and monitoring function. The Wi-Fi module enabled cost-effective solution has been made to protect the vehicles. This paper attempts to utilize two physically disjoint units in conjunction with each other, to provide a fool-proof mechanism against vehicle theft. A prototype has been made using Arduino and Wi-Fi module. Android smartphones are used to design for the user interface that allows access of the vehicle to an intended person only.

Evaluation of LiDAR data processing at the mobile network edge for connected vehicles

Abstract5G mobile network technology together with edge computing will create new opportunities for developing novel road safety services in order to better support connected and automated driving in challenging situations. This paper studies the feasibility and benefits of localized mobile network edge applications for supporting vehicles in diverse conditions. We study a particular scenario, where vehicle sensor data processing, required by road safety services, is installed into the mobile network edge in order to extend the electronic horizon of the sensors carried by other vehicles. Specifically, we focus on a LiDAR data-based obstacle warning case where vehicles receive obstacle warnings from the mobile network edge. The proposed solution is based on a generic system architecture. In this paper, we first evaluate different connectivity and computing options associated with such a system using ns-3 simulations. Then, we introduce a proof-of-concept implementation of the LiDAR-based obstacle warning scenario together with first results from an experimental evaluation, conducted both in a real vehicle testbed environment and in a laboratory setting. As a result, we obtain first insights on the feasibility of the overall solution and further enhancements needed.