Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy

Abstract

For self-driving systems or autonomous vehicles (AVs), accurate lane-level localization is a important for performing complex driving maneuvers. Classical GNSS-based methods are usually not accurate enough to have lane-level localization to support the AV’s maneuvers. LiDAR-based localization can provide accurate localization. However, the price of LiDARs is still one of the big issues preventing this kind of solution from becoming wide-spread commodity. Therefore, in this work, we propose a low-cost solution for lane-level localization using a vision-based system and a low-cost GPS to achieve high precision lane-level localization. Experiments in real-world and real-time demonstrate that the proposed method achieves good lane-level localization accuracy, outperforming solutions based on only GPS.

A Matched Case-Control Analysis of Autonomous vs Human-Driven Vehicle Accidents

“…adjust the vehicle's speed and trajectory within milliseconds, whereas humans may take several seconds to react 14 . Additionally, while rain can increase the likelihood of skidding or loss of control of a vehicle, AVs can employ consistent and precise sensing technologies such as cameras 31 , LiDAR 32 , radar 33 , and GPS 34 (note not all AVs necessarily have all these sensors) to detect and accurately perceive road conditions, regardless of the weather conditions 35 . In contrast, human drivers may have di culties seeing through heavy rain or fog, leading to a delay in detecting potential hazards or reacting appropriately.…”

Robust Visual-based Method and New Datasets for Ego-lane Index Estimation in Urban Environment

“…These methods can be divided into two categories based on the dependent map. The first category relies on the HD map [8,9]. These methods extracted semantic features from camera images and match semantic features with HD map to achieve ego-lane index estimation [10,11].…”