A LiDAR Assisted Control Module with High Precision in Parking Scenarios for Autonomous Driving Vehicle
Xin Xu, Yu Dong, Fan Zhu

TL;DR
This paper presents a LiDAR-assisted control module for autonomous vehicles that achieves high lateral precision in parking scenarios, outperforming original systems and expert human drivers.
Contribution
The paper introduces a novel control module with a real-time calibration algorithm and an error feedback system, significantly improving lateral precision in parking tasks.
Findings
Achieved lateral precision of 3±5 centimeters in parking scenarios.
Outperformed original Apollo modules in precision tasks.
Surpassed highly experienced human drivers in lateral control accuracy.
Abstract
Autonomous driving has been quite promising in recent years. The public has seen Robotaxi delivered by Waymo, Baidu, Cruise, and so on. While autonomous driving vehicles certainly have a bright future, we have to admit that it is still a long way to go for products such as Robotaxi. On the other hand, in less complex scenarios autonomous driving may have the potentiality to reliably outperform humans. For example, humans are good at interactive tasks (while autonomous driving systems usually do not), but we are often incompetent for tasks with strict precision demands. In this paper, we introduce a real-world, industrial scenario of which human drivers are not capable. The task required the ego vehicle to keep a stationary lateral distance (i.e. 3? <= 5 centimeters) with respect to a reference. To address this challenge, we redesigned the control module from Baidu Apollo open-source…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Robotics and Sensor-Based Localization · Robotic Path Planning Algorithms
MethodsAdaptive Parameter-wise Diagonal Quasi-Newton Method
