Abstract
Driving strategy in dynamic environment is crucial to the automated vehicle safety. In extremely emergency scenarios with unavoidable collision (UC), especially those with complex impact patterns, the potential crash risk should be well considered. This paper proposes a crash mitigation (CM) algorithm for UCs, which directly embeds a generalized crash severity index (CSI) model to vehicle-to-vehicle collisions of multiple impact patterns. The idea is that during the short time before a collision, the vehicle will actively adapt its position and poses to minimize the potential crash severity level after the collision. To this end, the generalized CSI model is introduced to estimate the potential crash severity of all sample paths, from which a crash-severity-optimal trajectory is obtained. To improve the inferring time efficiency of the planning module, a neural network is constructed and deployed to approximate the nonlinear severity model. The proposed algorithm is first validated through simulations of UC scenarios, including entry ramp merging, intersection crossing and downhill/uphill crossing. Then for the intersection crossing scenario, the algorithm is deployed to a real car and validated through digital-twin experiments. Results show that by combining the braking and steering interventions for better crash severity reduction, the proposed strategy can achieve better mitigation effects than commonly used collision avoidance (CA) strategies. This reveals that a new mindset of comprehensive safety strategy should not focus only on CA, but also the last resort of CM if collision is unavoidable. Our work may contribute as a promising solution to the safety problem in emergency scenarios.
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Abbreviations
- AI:
-
Artificial intelligence
- ADAS:
-
Advanced driving assist system
- AEB:
-
Automated emergency braking
- AES:
-
Automated emergency steering
- C.G:
-
Center of gravity
- CA:
-
Collision avoidance
- CM:
-
Crash mitigation
- CMI:
-
Crash momentum index
- CSI:
-
Crash severity index
- DOF:
-
Degrees of freedom
- EES:
-
Equivalent energy speed
- FEM:
-
Finite element method
- IPC:
-
Industrial personal computer
- MPC:
-
Model predictive control
- PDOF:
-
Principal direction of force
- POI:
-
Point of impact
- TTC:
-
Time to collision
- UC:
-
Unavoidable collision
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Author Contributions
Daofei Li and Zhaohan Hu contributed to the study conception and design. Vehicle modelling, crash severity modelling and planning algorithm were performed by Daofei Li and Zhaohan Hu. Control algorithm and simulation was done by Zhaohan Hu and Bin Xiao. Experiments, data collection and analysis were performed by Daofei Li, Zhaohan Hu, Jiajie Zhang and Bin Xiao. The first draft of the manuscript was written by Daofei Li and Zhaohan Hu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Department of Science and Technology of Zhejiang (No. 2022C01241).
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Li, D., Zhang, J., Xiao, B. et al. Vehicle crash mitigation strategy in unavoidable collision scenarios: focusing on motion planning by considering a generalized crash severity model. J Braz. Soc. Mech. Sci. Eng. 44, 581 (2022). https://doi.org/10.1007/s40430-022-03893-1
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DOI: https://doi.org/10.1007/s40430-022-03893-1