Research on Reliable and Safe Occupancy Grid Prediction in Underground Parking Lots

• URL: http://arxiv.org/abs/2407.02197v1
• Date: Tue, 2 Jul 2024 11:56:14 GMT
• Title: Research on Reliable and Safe Occupancy Grid Prediction in Underground Parking Lots
• Authors: JiaQi Luo,
• Abstract summary: This study tackles indoor autonomous driving, particularly in overlooked spaces like underground parking lots. Using CARLA's simulation platform, a realistic parking model is created for data gathering. An occupancy grid network then processes this data to predict vehicle paths and obstacles, enhancing the system's perception in complex indoor environments.
• Score: 0.5439020425819
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Against the backdrop of advancing science and technology, autonomous vehicle technology has emerged as a focal point of intense scrutiny within the academic community. Nevertheless, the challenge persists in guaranteeing the safety and reliability of this technology when navigating intricate scenarios. While a substantial portion of autonomous driving research is dedicated to testing in open-air environments, such as urban roads and highways, where the myriad variables at play are meticulously examined, enclosed indoor spaces like underground parking lots have, to a significant extent, been overlooked in the scholarly discourse. This discrepancy highlights a gap in derstanding the unique challenges these confined settings pose for autonomous navigation systems. This study tackles indoor autonomous driving, particularly in overlooked spaces like underground parking lots. Using CARLA's simulation platform, a realistic parking model is created for data gathering. An occupancy grid network then processes this data to predict vehicle paths and obstacles, enhancing the system's perception in complex indoor environments. Ultimately, this strategy improves safety in autonomous parking operations. The paper meticulously evaluates the model's predictive capabilities, validating its efficacy in the context of underground parking. Our findings confirm that the proposed strategy successfully enhances autonomous vehicle performance in these complex indoor settings. It equips autonomous systems with improved adaptation to underground lots, reinforcing safety measures and dependability. This work paves the way for future advancements and applications by addressing the research shortfall concerning indoor parking environments, serving as a pivotal reference point.

Related papers

• A Computer Vision Approach for Autonomous Cars to Drive Safe at Construction Zone [0.0]
  A car equipped with an autonomous driving system (ADS) comes with various cutting-edge functionalities such as adaptive cruise control, collision alerts, automated parking, and more. This paper presents an innovative and highly accurate road obstacle detection model utilizing computer vision technology that can be activated in construction zones and functions under diverse drift conditions.
  arXiv  Detail & Related papers  (2024-09-24T07:11:00Z)
• Acceleration method for generating perception failure scenarios based on editing Markov process [0.0]
  This study proposes an accelerated generation method for perception failure scenarios tailored to the underground parking garage environment. The method generates an intelligent testing environment with a high density of perception failure scenarios. It edits the Markov process within the perception failure scenario data to increase the density of critical information in the training data.
  arXiv  Detail & Related papers  (2024-07-01T05:33:48Z)
• Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers? [60.51287814584477]
  This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs. We develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios.
  arXiv  Detail & Related papers  (2024-05-14T09:42:21Z)
• Scalable Decentralized Cooperative Platoon using Multi-Agent Deep Reinforcement Learning [2.5499055723658097]
  This paper introduces a vehicle platooning approach designed to enhance traffic flow and safety. It is developed using deep reinforcement learning in the Unity 3D game engine. The proposed platooning model focuses on scalability, decentralization, and fostering positive cooperation.
  arXiv  Detail & Related papers  (2023-12-11T22:04:38Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• SHINE: Deep Learning-Based Accessible Parking Management System [1.7109513360384465]
  An increase in the number of privately owned vehicles has led to the abuse of disabled parking spaces. Traditional license plate recognition (LPR) systems have proven inefficient in addressing such a problem in real-time. We have proposed a novel system called, Shine, which uses the deep learning-based object detection algorithm for detecting the vehicle.
  arXiv  Detail & Related papers  (2023-02-02T02:46:52Z)
• Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
  In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle. In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
  arXiv  Detail & Related papers  (2022-07-05T16:33:20Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving [100.57791628642624]
  We introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving. We design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent.
  arXiv  Detail & Related papers  (2022-03-04T16:14:33Z)
• Smart Parking Space Detection under Hazy conditions using Convolutional Neural Networks: A Novel Approach [0.0]
  This paper investigates the use of dehazing networks that improves the performance of parking space occupancy under hazy conditions. The proposed system is deployable as part of existing smart parking systems where limited number of cameras are used to monitor hundreds of parking spaces.
  arXiv  Detail & Related papers  (2022-01-15T14:15:46Z)
• Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving [104.32241082170044]
  We study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map. Our approach is the first one that can predict the existence of unseen vehicles in most cases.
  arXiv  Detail & Related papers  (2021-09-03T13:33:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.