Custom distribution for sampling-based motion planning

Flores-Aquino, Gabriel O.; Vasquez-Gomez, J. Irving; Gutiérrez-Frías, Octavio

doi:10.1007/s40430-022-03379-0

Cited by 4 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…obstacle avoidance. [10] Different curves are used to better fit the kinematics characteristics of different vehicles. Some researchers use the Dubins curve, an asymmetric curve, to smooth the piecewise path generated by algorithms like RRT [21].…”

Section: Related Workmentioning

confidence: 99%

“…However, when it comes to evasive steering, since the vehicle direction and speed are both controlled within very strict constraints of time and space, the activation of AES functions is asking for a comprehensive and reliable algorithm of motion planning. On the other hand, most motion planning algorithms focus on how to avoid obstacles in potentially dangerous scenarios [4][5][6][7][8][9][10][11], while very few have contributed to how to reduce the crash injury in unavoidable collisions via motion planning [12][13][14][15][16]. For the example in Fig.…”

Section: Introduction 1motivationmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle crash mitigation strategy in unavoidable collision scenarios: focusing on motion planning by considering a generalized crash severity model

Zhang

Xiao

et al. 2022

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

Driving strategy in dynamic environment is crucial to the automated vehicle safety. In extremely emergency scenarios with unavoidable collisions, especially those with complex impact patterns, the potential crash risk should be well considered. This paper proposes a crash mitigation algorithm for unavoidable collisions, which directly embeds a generalized crash severity index 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 crash severity index (CSI) model is introduced to estimate the potential crash severity of all sample paths, from which a crash-severityoptimal 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 unavoidable collision scenarios, Vehicle Crash Mitigation Considering CSI model 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 strategies. This reveals that a new mindset of comprehensive safety strategy should not focus only on collision avoidance, but also the last resort of crash mitigation if collision is unavoidable. Our work may contribute as a promising solution to the safety problem in emergency scenarios.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introduction 1motivationmentioning

confidence: 99%

Vehicle crash mitigation strategy in unavoidable collision scenarios: focusing on motion planning by considering a generalized crash severity model

Zhang

Xiao

et al. 2022

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…On the other hand, most motion planning algorithms focus on how to avoid obstacles in potentially dangerous scenarios [4][5][6][7][8][9][10][11], while very few have contributed to how to reduce the crash injury in unavoidable collisions via motion planning [12][13][14][15][16]. For the example in Fig.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Motion Planning for Crash Mitigation in Unavoidable Collision Scenarios

Li¹,

Zhang²,

Xiao³

et al. 2022

Preprint

View full text Add to dashboard Cite

<div>Motion planning in dynamic environment is crucial to the automated driving safety. In extremely emergency scenarios with unavoidable collisions, especially those with complex impact patterns, the potential crash risk should be well considered in motion planning. This paper proposes a motion planning algorithm for unavoidable collisions, which directly embeds a generalized crash severity index model to vehicle-to-vehicle collisions of multiple impact patterns. Firstly, the clothoid curve is used to sample the vehicle trajectory before collision, and a two-degree-of-freedom model is adopted to predict the vehicle poses corresponding to each sample path. Then, the crash severity index model is to estimate the potential crash severity of all sample paths. To improve the inferring time efficiency, a neural network is constructed and deployed to approximate the nonlinear severity model. Finally, the crash-severity-optimal trajectory is tracked through model predictive control method. 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 strategies. The deployment of real car experiment and sensitivity analysis demonstrate that the planning algorithm can guarantee </div>real-time and reliably safe performances.

show abstract