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Evaluation Model of Autonomous Vehicles' Speed Suitability Based on Overtaking Frequency
Shiwu Li1, Mengyuan Huang1, Mengzhu Guo1,2
1School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China.
Sensors (Basel, Switzerland)
|January 12, 2021
Summary
Autonomous vehicles can now better judge speed using overtaking frequency (OTF), a new traffic flow metric. This advanced algorithm, utilizing long short-term memory (LSTM) networks, achieved 93% accuracy in road tests.
Area of Science:
- Autonomous Driving Systems
- Artificial Intelligence in Transportation
- Traffic Flow Analysis
Background:
- Human drivers intuitively assess speed based on experience and traffic.
- Autonomous vehicles lack the ability to self-assess speed suitability using environmental cues.
- Existing systems struggle to adapt speed based on surrounding traffic dynamics.
Purpose of the Study:
- To develop a novel method for autonomous vehicles to evaluate their speed relative to traffic.
- To introduce the Overtaking Frequency (OTF) parameter for assessing traffic flow.
- To create an accurate speed evaluation algorithm for driverless cars.
Main Methods:
- Introduced Overtaking Frequency (OTF) based on traffic flow on both sides of the lane.
- Developed a speed evaluation algorithm using a Long Short-Term Memory (LSTM) model.
- Utilized OTF, vehicle longitudinal motion, and leading vehicle comparison as input variables for the LSTM model.
Main Results:
- The proposed algorithm achieved a 93% accuracy rate in real-world road tests.
- The inclusion of OTF as an observation variable significantly improved speed judgment accuracy.
- The hierarchical method within the algorithm effectively judged vehicle velocity.
Conclusions:
- Overtaking Frequency (OTF) is a valuable parameter for enhancing autonomous driving perception.
- LSTM-based algorithms incorporating OTF can accurately assess vehicle speed in complex traffic.
- This research contributes to safer and more efficient autonomous driving systems.
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