Advancements in Sensing and Perception for Autonomous Vehicles in Adverse Environmental Conditions

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 16 August 2024 | Viewed by 190

Special Issue Editor

Department of Psychology, Stanford University, Stanford, CA 94305, USA
Interests: autonomous vehicles; imaging systems; autonomous driving; computer graphics

Special Issue Information

Dear Colleagues,

The evolution of autonomous vehicles is inextricably linked to the sophistication of their perception systems. These systems, which represent a fusion of advanced sensor technologies and computational algorithms, serve as the cornerstone for autonomous path planning and control. They utilize a wide array of sensors, such as cameras, LiDAR, RADAR, and ultrasonic sensors, integrated with state-of-the-art signal processing algorithms and machine learning frameworks. The primary challenge lies in accurately and efficiently processing and interpreting sensor data, which is essential for real-time path planning and control in varying operational environments.

Specifically, a significant and persistent challenge in this field lies in the performance of perception systems under adverse weather conditions, such as heavy rain, fog, snow, and varying light intensities. These conditions can severely impede the abilities of sensors, leading to reduced visibility and data fidelity. Moreover, the challenge includes ensuring data integrity and reliability in such environments, which necessitates robust sensor fusion techniques and adaptive algorithms capable of compensating for environmental variabilities.

Topics of Interest:

  1. Enhanced Sensor Technologies: Research that investigates novel sensor designs, particularly those that enhance operational resilience and accuracy in adverse weather conditions, is crucial. This includes advancements in sensor data restoration, spectral sensitivity, and noise reduction techniques both via traditional signal processing and machine learning-based algorithms.
  2. AI and Deep Learning in Data Interpretation: Research that focuses on the application of advanced deep learning models for improved object detection, scene understanding, and decision-making processes in complex and dynamic environments.
  3. Sensor Fusion and Data Integration: Research that focuses on the methodologies and frameworks for effective sensor fusion, aiming to create a comprehensive and resilient perception mechanism that mitigates the limitations of individual sensors, particularly in challenging weather conditions.
  4. Real-Time Data Processing Architectures: Research that explores innovative data processing architectures and algorithms that enhance the real-time capabilities of autonomous vehicles for fast response, focusing on computational efficiency, latency reduction, and energy optimization.
  5. Simulation, Validation, and Robustness Testing: Contributions that present novel approaches to simulating and validating perception systems under varied and adverse conditions, ensuring system robustness and reliability.
  6. End-to-End System Optimization: Research on optimizing the entire perception system of autonomous vehicles for efficiency, accuracy, and reliability. Contributions may include topics like sensor parameter optimization, system-level optimization, and the balance between hardware capabilities and software demands.
  7. Case Studies and Applications: We also welcome the submission of case studies and practical applications demonstrating the real-world implementation of these technologies in autonomous vehicles.

We are inviting you to contribute articles, perspectives, and reviews addressing the abovementioned topics, offering both theoretical insights and practical solutions to the challenges faced in optimizing perception systems for autonomous vehicles. This includes advancements in both hardware and algorithms, with a special focus on performance in adverse weather conditions. Your research and expertise are invaluable and will significantly contribute to advancing this critical field within automotive technology.

Technical Program Committee Member

Name: Dr. Chuxi Yang
Affiliation: School of Control Science and Engineering, Dalian University of Technology, Dalian 116023, China
Research Interests: computational imaging simulation; computer vision; digital image processing

Dr. Zhenyi Liu
Guest Editor

Manuscript Submission Information

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  • autonomous driving
  • sensing technology
  • simulation and validation
  • realtime data processing
  • sensor fusion
  • system integration
  • end-to-end optimization
  • sensor data restoration

Published Papers

This special issue is now open for submission.
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