LiDAR (light detection and ranging) is an active remote sensing technique,analogous to radar, but using laser light. LiDAR instruments measure the roundtrip time for a pulse of laser energy to travel between the sensor and a target. While the technology is still maturing, the aging US infrastructure, which also faces funding uncertainty, is being pressured by continuously and quickly evolving AV technology. Fortunately, although automation is posing challenges, it is also revitalizing the conversation around infrastructure and its role in the transportation ecosystem.
This paper provides a brief background on LiDAR remote sensing, and its current and projected uses in Tesla on how they have updated their current algorithm to a new one to analyze those laser bursts and their subsequent echoes to figure out whether they’re hitting raindrops or snowflakes. The levels of automation for AVs, and the sensing suites used to perceive the environment, including the surrounding infrastructure. It explains how the current infrastructure can be modified to improve AV performance, and then reviews challenges to continued progress.
Snow and Rain are confusing LiDAR sensors and also cameras. LiDAR refers to the light sensing radar that uses lasers to map the car’s surroundings so it can see the surroundings of the car. When there’s snow on the ground, the cars’ LiDAR sensor and camera have a difficult time seeing the lane markers and other markers that help them drive safely. As a consultant I was asked to find out a plan to fix this issue. The solution to this issue is to create high-resolution 3D maps that come with information not only about the road, but also what’s above the road, like its topography and nearby signs and landmarks. This way, when the car can’t see lane markings, it can use landmarks to pinpoint itself on the map. Autonomous cars rely on LiDAR sensors that emit short bursts of lasers as the car drives along. The car pieces together these laser bursts to create a high-resolution 3D map of the environment. The new algorithm which is introduced, allows the car to analyze those laser bursts and their subsequent echoes to figure out whether they’re hitting raind or snow. When a laser goes through the rain or snow, part of it will hit a rain or snow, and the other part will likely be diverted towards the ground. The algorithm, by listening to the echoes from the diverted lasers, builds up a picture of the “ground plane” as a result.
Current infrastructure is designed and built to accommodate human abilities and information needs. Road signs, for example, are sized and positioned based on human perception capabilities in relation to speed limits and local traffic patterns. To align with advances in AV technologies, the infrastructure will likely need to evolve in three ways: (1) account for AV sensing capabilities, (2) provide complementary sensing capabilities, and (3) adapt to the requirements of transportation modes enabled by AVs. AV technologies are currently being designed to operate with little or no support from the infrastructure, but the burden of perception and path planning will be increasingly shared and integrated with the infrastructure. Some argue that AVs should be capable of navigating using the same infrastructure that human drivers use today. But technologies can equip AVs with sensing range and accuracy beyond human drivers’ capabilities.
For instance, humans drive with limited exchanges of information with other human drivers, but vehicle-to-vehicle communication can facilitate AV navigation and planning by sharing information, even in the absence of line of sight. Deeper integration of vehicles and infrastructure will increase AV sensitivity to infrastructure conditions and inconsistencies, while at the same time granting additional layers of robustness, making AVs arguably safer. Certain physical infrastructure elements such as lane markings, signage, and signals can be designed to facilitate AV perception and interpretation. Infrastructure can also act as a distributed sensor network, supporting data sharing and providing information to vehicles. And technologies such as variable speed limits, traffic detection at signalized intersections, and traffic signal coordination are already moving the infrastructure in this direction. It is expected that this digital infrastructure will become the cyberphysical backbone for AVs: using an Internet of Things approach, it will be capable of sensing the environment and sharing useful information with vehicles For instance, precipitation sensors may alert AVs to potentially hazardous driving conditions, and smart traffic cones may be capable of repositioning themselves safely on the road while communicating to nearby vehicles about their placement and the reason for their presence.
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