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Conference Paper
Risk Assessment for Automatic Lane Change Maneuvers on Highways
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- Authors
- Samyeul Noh, Kyounghwan An
- Issue Date
- 2017-05
- Citation
- International Conference on Robotics and Automation (ICRA) 2017, pp.247-254
- Language
- English
- Type
- Conference Paper
- Project Code
- 17HS1800, Development of Driving Computing System Supporting Real-time Sensor Fusion Processing for Self-Driving Car, Kim Sung Hoon
- Abstract
- This paper presents a risk assessment algorithm for automatic lane change maneuvers on highways. It is capable of reliably assessing a given highway situation in terms of the possibility of collisions and robustly giving a recommendation for lane changes. The algorithm infers potential collision risks of observed vehicles based on Bayesian networks considering uncertainties of its input data. It utilizes two complementary risk metrics (time-to-collision and minimal safety margin) in temporal and spatial aspects to cover all risky situations that can occur for lane changes. In addition, it provides a robust recommendation for lane changes by filtering out uncertain noise data pertaining to vehicle tracking. The validity of the algorithm is tested and evaluated on public highways in real traffic as well as a closed high-speed test track in simulated traffic through in-vehicle testing based on overtaking and overtaken scenarios in order to demonstrate the feasibility of the risk assessment for automatic lane change maneuvers on highways.
- KSP Keywords
- Automatic lane change, Bayesian Network(BN), High Speed, In-vehicle, Risk assessment, Safety margin, Test track, Time-to-Collision, Uncertain noise, Vehicle testing, assessment algorithm