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 5603
Special Issue Editors
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
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
