Outdoor Traffic Scene Risk Estimation in the Context of Autonomous Driving
- Resource Type
- Conference
- Authors
- Brehar, Raluca Didona; Babut, Rares Ovidiu; Fuzes, Attila; Danescu, Radu
- Source
- 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP) Intelligent Computer Communication and Processing (ICCP), 2022 IEEE 18th International Conference on. :129-134 Sep, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Deep learning
Visualization
Machine learning algorithms
Roads
Estimation
Benchmark testing
Feature extraction
- Language
- ISSN
- 2766-8495
An outdoor pedestrian-related risk estimation framework for traffic scenes using low cost visual sensors is proposed. The framework includes a Jetson Nano device with a monocular color camera for scene perception and processing. The risk estimation algorithm combines deep learning based approaches for road segmentation and object detection with conventional machine learning algorithms such as Random Forest, Support Vector Machines, Decision Trees, AdaBoost trained on visual and motion features computed for each frame. For evaluating the proposed algorithm a benchmark dataset for joint attention in autonomous driving and a custom dataset were annotated with the risk level each frame. The results show an accuracy of 90% for the benchmark data and 80% for the custom data series.