Autonomous Driving without a Burden: View from Outside with Elevated LiDAR

Abstract

The current autonomous driving architecture places a heavy burden in signal processing for the graphics processing units (GPUs) in the car. This directly translates into battery drain and lower energy efficiency, crucial factors in electric vehicles. This is due to the high bit rate of the captured video and other sensing inputs, mainly due to Light Detection and Ranging (LiDAR) sensor at the top of the car which is an essential feature in autonomous vehicles. LiDAR is needed to obtain a high precision map for the vehicle AI to make relevant decisions. However, this is still a quite restricted view from the car. This is the same even in the case of cars without a LiDAR such as Tesla. The existing LiDARs and the cameras have limited horizontal and vertical fields of visions. In all cases it can be argued that precision is lower, given the smaller map generated. This also results in the accumulation of a large amount of data in the order of several TBs in a day, the storage of which becomes challenging. If we are to reduce the effort for the processing units inside the car, we need to uplink the data to edge or an appropriately placed cloud. However, the required data rates in the order of several Gbps are difficult to be met even with the advent of 5G. Therefore, we propose to have a coordinated set of LiDAR's outside at an elevation which can provide an integrated view with a much larger field of vision (FoV) to a centralized decision making body which then sends the required control actions to the vehicles with a lower bit rate in the downlink and with the required latency. The calculations we have based on industry standard equipment from several manufacturers show that this is not just a concept but a feasible system which can be implemented.The proposed system can play a supportive role with existing autonomous vehicle architecture and it is easily applicable in an urban area.

Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview

“…An example of an ITS crowdsourcing technology that requires high computational power and storage is the use of shared LIDAR for autonomous vehicles. Elevated LiDAR (ELiD) [35] is touted as an alternative or at least complementary system to LiDAR units placed on-board, in order to reduce the amount of computational and cost overhead required to safely operate an autonomous vehicle. One challenge for ELiD systems is to allocate their real-time generated data to edge and cloud servers for processing of the raw point cloud data in order to minimize the latency and speed up their operation to provide vehicles instantaneous information, e.g., 3D mapping.…”

“…The summary of our findings are shown in Table 1. Provides a trust management mechanism as a framework to secure vehicular social data, a cloud-based architecture for VSNs X [14] Advanced integrated sensing for autonomous vehicles X [2,4] Survey of VSN X [28,29] Distributed architecture for mobile crowdsourcing management X [3] UAVs in the ITS: their role in increasing network connectivity X [22][23][24] RSU placement: optimal, heuristic, and adjustment to unexpected events X X X [35] Proposal of automated elevated LiDAR to reduce autonomous vehicle computational overhead X X Applications [15] Crowdsourced monitoring and reporting of available street parking X X X [20] Crowdsourced data collection of road lane data X [21] Convolutional neural networks applied for lane detection X [8,9] Social media-based traffic prediction with NLP X [6] Social media data mining applied to detect roadway incidents X [7] Development and testing of a last-mile navigation system to add to existing mapping programs X [19] Infrastructure health monitoring X X [18] Advanced tracking of public transportation vehicles X [16,17] Generating a 3D map of surroundings based on crowdsourced data X [4] Utilization of mobile devices on fixed bus routes to measure and visualize traffic congestion X X Table 1. Cont.…”

“…In addition, we considered VSN components. We looked at the planning of RSU installation [22][23][24], along with UAVs as flying RSUs [3], trusted cloud computing systems [32], automated sensing systems [14,35], and decentralized control/access systems [28,29].…”

Designing and Implementing a Reproducible Log System Using Basic Safety Message in a V2V Environment

“…There are situations where objects in the vicinity of the vehicle, such as on the side or rear, are not accurately detected owing to the physical limitations of the radar/LiDAR and sensors (Lee et al, 2020). Consequently, there is an insufficiency of data for the computing system to evaluate and determine the precise response to control the system (Jayaweera et al, 2019). V2X technology can be used to address this issue by supplementing the data obtained by the physical sensors (Cha et al, 2018;Kim et al, 2013), through the utilization of situational information obtained via other methods.…”

Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach

“…Some AV designs even incorporate multiple, expensive LiDARs to gain an even higher degree of seeing capability based on sensor redundancy [4]. Unfortunately, the market price of LiDARs remains substantial [5]. For example, a 16-beam Velodyne LiDAR costs almost $8,000 [6].…”