Motion Planning for Collision Mitigation via FEM–Based Crash Severity Maps

Simon, Bruno; Franke, Florian; Riegl, Peter; Gaull, Andreas

doi:10.1109/ivs.2019.8813832

Cited by 20 publications

(14 citation statements)

References 15 publications

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“…The first collision is determined at t c = 0.853s between the ego vehicle and the other vehicle (I), and the colliding trajectories are presented with black boxes in Fig. (11). The collision at this time for trajectory 291 is presented in more detail in the top right corner of this figure.…”

Section: B Multi-vehicle Scenariomentioning

confidence: 99%

“…The crash severity is part of the formulation of the MPC cost function and is based on relative speed, relative heading angle and mass ratio between the vehicles. In [11], a crash severity map was developed by finite elements (FEM) simulations that aimed at identifying a crash configuration minimizing the crash severity. The method searches for a maneuver that leads to this crash configuration, which is determined by lateral offset and the relative heading angle.…”

Section: Introductionmentioning

confidence: 99%

“…The method searches for a maneuver that leads to this crash configuration, which is determined by lateral offset and the relative heading angle. The severity of a crash in [11] is based on the deformation of nodes in the passenger compartment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Data-Driven Method Towards Minimizing Collision Severity for Highly Automated Vehicles

Parseh

Asplund

Svensson

et al. 2021

IEEE Trans. Intell. Veh.

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The deployment of autonomous vehicles on public roads calls for the development of methods that are reliably able to mitigate injury severity in case of unavoidable collisions. This study proposes a data-driven motion planning method capable of minimizing injury severity for vehicle occupants in unavoidable collisions. The method is based on establishing a metric that models the relationship between impact location and injury severity using real accident data, and subsequently including it in the cost function of a motion planning framework. The vehicle dynamics and associated constraints are considered through a precomputed trajectory library, which is generated by solving an optimal control problem. This allows for efficient computation as well as an accurate representation of the vehicle. The proposed motion planning approach is evaluated by simulation, and it is shown that the trajectory associated with the minimum cost mitigates the collision severity for occupants of passenger vehicles involved in the collision.

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Section: B Multi-vehicle Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Data-Driven Method Towards Minimizing Collision Severity for Highly Automated Vehicles

Parseh

Asplund

Svensson

et al. 2021

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

show abstract

“…Considering the actuator limits of the experiment vehicle, a maximum front steering wheel angle is set as 25 °, maximum steering rate as 50°/s , maximum braking deceleration as -0.7g, and maximum acceleration as 0.5g. (10) The desired system outputs 𝒚 𝑑𝑒𝑠 are the lateral position 𝑦 𝑑𝑒𝑠 and the yaw angle 𝜃 𝑑𝑒𝑠 , i.e. 𝒚 𝑑𝑒𝑠 = [𝑦 𝑑𝑒𝑠 𝜃 𝑑𝑒𝑠 ] T .…”

Section: E Model Predictive Control (Mpc) For Path Trackingmentioning

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 [1][2][3][4][5][6], while very few have contributed to how to reduce the crash injury in unavoidable collisions via motion planning [7][8][9][10][11]. For the example in Fig.…”

mentioning

confidence: 99%

Motion Planning of Vehicles for Crash Mitigation in Emergency Driving Scenarios

Li¹,

Hu²

2021

Preprint

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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 real-time and reliably safe performances.

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