Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) , Volume II

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 5505

Special Issue Editors

Department of Electrical and Computer Engineering, Mississippi State University, 406 Hardy Road, 216 Simrall Hall, Mississippi State, MS 39762, USA
Interests: Advanced Driver Assistance Systems (ADAS); scene understanding; sensor processing (Radar, LiDAR, camera, hyperspectral, thermal); machine learning; digital image and signal processing
Special Issues, Collections and Topics in MDPI journals
School of Automotive Studies, Tongji University, Shanghai 201804, China
Interests: smart driving and smart car sharing travesmart driving and smart car sharing travel; smart and new energy automobile industry strategy and policy; automobile industry big data analysis; automobile product strategy; automobile marketing

Special Issue Information

Dear Colleagues,

Advanced Driver Assistance Systems (ADAS) are being integrated into more and more vehicles, which offer enhanced safety (collision avoidance, route following, obstacle detection, automatic braking), driver assistance (lane keeping, lane following, adaptive cruise control), etc. Fully autonomous vehicles are still not fully available and much research is being conducted in these areas. Three main things are driving this revolution: (1) The availability of inexpensive sensors such as cameras, LiDARs, automotive radars, etc. (2) advanced machine learning methods such as deep learning, and (3) inexpensive and highly capable computing platforms that can handle large amounts of data and processing, utilizing both CPUs and GPUs.

This Special Issue aims to cover the most recent advances in autonomous and automated vehicles of all kinds (commercial, industrial) including their interaction with other vehicles, road users or infrastructure. Novel theoretical approaches or practical applications of all aspects of ADAS systems are welcomed. Reviews and surveys of the state-of-the-art are also welcomed. Topics of interest to this Special Issue include, but are not limited to, the following topics:

  • Deep learning and machine learning in ADAS systems
  • Intelligent navigation and localization
  • Scene understanding (e.g., driver intent, pedestrian intent, etc.)
  • Obstacle detection, classification, and avoidance
  • Pedestrian and bicyclist detection, classification, and avoidance
  • Vehicle detection and avoidance
  • Animal detection, classification, and avoidance
  • Object tracking
  • Road traffic sign detection and classification
  • Autonomous parking
  • Multi-sensor data processing and data fusion
  • Collision avoidance algorithms
  • Actuation systems for autonomous vehicles
  • Vehicle-to-vehicle and vehicle-to-infrastructure communication
  • Advanced vehicle control systems
  • Optimal maneuver algorithms
  • Real-time embedded control systems
  • Computing platforms and running complex ADAS software in real-time
  • Perception in challenging conditions
  • Dynamic path planning algorithms

Dr. John Ball
Dr. Ning Wang
Guest Editors

Manuscript Submission Information

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Keywords

  • Deep learning and machine learning in ADAS systems
  • Intelligent navigation and localization
  • Scene understanding (e.g. driver intent, pedestrian intent, etc.)
  • Obstacle detection, classification, and avoidance
  • Pedestrian and bicyclist detection, classification, and avoidance
  • Vehicle detection and avoidance
  • Animal detection, classification, and avoidance
  • Object tracking
  • Road traffic sign detection and classification
  • Autonomous parking
  • Multi-sensor data processing and data fusion
  • Collision avoidance algorithms
  • Actuation systems for autonomous vehicles
  • Vehicle-to-vehicle and vehicle-to-infrastructure communication
  • Advanced vehicle control systems
  • Optimal maneuver algorithms
  • Real-time embedded control systems
  • Computing platforms and running complex ADAS software in real-time
  • Perception in challenging conditions
  • Dynamic path planning algorithms

Published Papers (2 papers)

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Research

16 pages, 1947 KiB  
Article
Multi-Camera Vehicle Tracking Based on Deep Tracklet Similarity Network
by Yun-Lun Li, Hao-Ting Li and Chen-Kuo Chiang
Electronics 2022, 11(7), 1008; https://doi.org/10.3390/electronics11071008 - 24 Mar 2022
Viewed by 2084
Abstract
Multi-camera vehicle tracking at the city scale has received lots of attention in the last few years. It has large-scale differences, frequent occlusion, and appearance differences caused by the viewing angle differences, which is quite challenging. In this research, we propose the Tracklet [...] Read more.
Multi-camera vehicle tracking at the city scale has received lots of attention in the last few years. It has large-scale differences, frequent occlusion, and appearance differences caused by the viewing angle differences, which is quite challenging. In this research, we propose the Tracklet Similarity Network (TSN) for a multi-target multi-camera (MTMC) vehicle tracking system based on the evaluation of the similarity between vehicle tracklets. In addition, a novel component, Candidates Intersection Ratio (CIR), is proposed to refine the similarity. It provides an associate scheme to build the multi-camera tracking results as a tree structure. Based on these components, an end-to-end vehicle tracking system is proposed. The experimental results demonstrate that an 11% improvement on the evaluation score is obtained compared to the conventional similarity baseline. Full article
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21 pages, 1580 KiB  
Article
Adaptive Fuzzy PID Control Strategy for Vehicle Active Suspension Based on Road Evaluation
by Shi-Yuan Han, Jia-Feng Dong, Jin Zhou and Yue-Hui Chen
Electronics 2022, 11(6), 921; https://doi.org/10.3390/electronics11060921 - 16 Mar 2022
Cited by 32 | Viewed by 2896
Abstract
The paper proposes a fuzzy proportion integral differential (PID) control strategy based on road estimation to meet the different performance requirements of active suspension for comfort and safety under different road conditions in view of the uncertainty of vehicle suspension parameters and random [...] Read more.
The paper proposes a fuzzy proportion integral differential (PID) control strategy based on road estimation to meet the different performance requirements of active suspension for comfort and safety under different road conditions in view of the uncertainty of vehicle suspension parameters and random road perturbations. First, flexible neural tree (FNT) is used to estimate the vertical disturbance of road within uncertain vehicle parameters in which the Fourier transform of the autocorrelation function is used to fit the road power spectrum density (PSD). In order to compensate for the delay and realize the real-time estimate of road conditions, an online fuzzy evaluation strategy for road condition is developed by integrating the fitted road PSD and the real-time suspension performance. After that, an adaptive fuzzy PID control strategy is developed based on the adaptive suspension control performance, which is supposed to realize the adaptive adjustment of control parameters for different road conditions. The simulation experimental results show that the online fuzzy evaluation strategy can effectively reflect the change in road condition, and the proposed adaptive fuzzy PID control strategy can work for adjusting the parameters adaptively, according to the changes in road conditions and therefore meet the control performance requirements under different road conditions. Full article
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