Vehicle posture control through aggressive maneuvering for mitigation of T-bone collisions

Chakraborty, Imon; Tsiotras, Panagiotis; Lǚ, Jianbo

doi:10.1109/cdc.2011.6161241

Cited by 16 publications

(18 citation statements)

References 15 publications

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“…Optimal control problems for vehicles in time-critical situations have been studied previously, see [8,12,18,19] for different examples concerning T-bone collisions and cornering. The influence of the road surface and the car transmission layout was investigated in [20] using a ST chassis model.…”

Section: Introductionmentioning

confidence: 99%

Models and methodology for optimal trajectory generation in safety-critical road–vehicle manoeuvres

Berntorp

Olofsson

Lundahl

et al. 2014

Vehicle System Dynamics

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There is currently a strongly growing interest in obtaining optimal control solutions for vehicle maneuvers, both in order to understand optimal vehicle behavior and, perhaps more importantly, to devise improved safety systems, either by direct deployment of the solutions or by including mimicked driving techniques of professional drivers. However, it is nontrivial to find the right combination of models, optimization criteria, and optimization tools to get useful results for the above purposes. Here, a platform for investigation of these aspects is developed based on a state-of-the-art optimization tool together with adoption of existing vehicle chassis and tire models. A minimum-time optimization criterion is chosen to the purpose of gaining insight in at-the-limit maneuvers, with the overall aim of finding improved fundamental principles for future active safety systems. The proposed method to trajectory generation is evaluated in time-critical maneuvers using vehicle models established in literature. We determine the optimal control solutions for three maneuvers, using tire and chassis models of different complexity. The results are extensively analyzed and discussed. Our main conclusion is that the tire model has a fundamental influence on the resulting control inputs. Also, for some combinations of chassis and tire models, inherently different behavior is obtained. However, certain variables important in vehicle safety-systems, such as the yaw moment and the body-slip angle, are similar for several of the considered model configurations in aggressive maneuvering situations.

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Section: Introductionmentioning

confidence: 99%

Models and methodology for optimal trajectory generation in safety-critical road–vehicle manoeuvres

Berntorp

Olofsson

Lundahl

et al. 2014

Vehicle System Dynamics

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show abstract

“…Consequently, different approaches to model calibration were investigated prior to the study. One approach would be to only scale the friction coefficients µ x and µ y , as done in (Chakraborty et al, 2011). However, the peaks in the tire-force surfaces occur at different lateral and longitudinal slip combinations, see Figs.…”

Section: Comparison Of Control Strategiesmentioning

confidence: 99%

“…Even though the vehicle and tire models utilized in this paper are similar to those presented in the mentioned references, previous research approaches focus on a particular vehicle model on a specific surface. Comparisons of optimal control maneuvers for different road conditions have been made, see (Chakraborty et al, 2011), but are limited to varying the friction coefficient, and we show that important tire-force characteristics might be neglected with that approach. To the best of our knowledge, no comprehensive approach to perform comparisons of optimal control maneuvers for different road conditions has been made, which motivates the study presented here.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Optimal Vehicle Maneuvers for Different Road Conditions

Olofsson

Lundahl

Berntorp

et al. 2013

IFAC Proceedings Volumes

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Abstract:We investigate optimal maneuvers for road-vehicles on different surfaces such as asphalt, snow, and ice. The study is motivated by the desire to find control strategies for improved future vehicle safety and driver assistance technologies. Based on earlier presented measurements for tireforce characteristics, we develop tire models corresponding to different road conditions, and determine the time-optimal maneuver in a hairpin turn for each of these. The obtained results are discussed and compared for the different road characteristics. Our main findings are that there are fundamental differences in the control strategies on the considered surfaces, and that these differences can be captured with the adopted modeling approach. Moreover, the path of the vehicle center-of-mass was found to be similar for the different cases. We believe that these findings imply that there are observed vehicle behaviors in the results, which can be utilized for developing the vehicle safety systems of tomorrow.

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“…Such AS features focus on avoiding accidents or mitigating injury/damage due to unavoidable accidents through various vehicle controls. Notable AS features in production 31 include electronic stability control (ESC) 32 , roll stability control 33 , collision mitigation by braking, curve control, emergency steering assist [34][35] , and multi-collision braking [36][37] .…”

Section: Driver Assistance and Active Safety Agentmentioning

confidence: 99%

Future Mobility

Filev¹,

Lǚ²,

Hrovat³

2013

Mechanical Engineering

Self Cite

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This article presents an automotive control approach for information-rich future mobility. It integrates in-vehicle networked controls with cloud computing accessible through a wireless network to elevate current on-board controls to a new level for additional benefits and performance. Outsourcing computation-intensive tasks to a cloud-computing server is an extension of the current server-based concierge/infotainment type features. While in-vehicle controls remain essential for safety critical and real-time functionality, the cloud-computing paradigm offers another degree of freedom for control system design. In future vehicle controls, the cloud can be used for very demanding computations that otherwise cannot be accomplished by on-board electronic control units (ECUs), especially for information-intensive tasks. The so-called local-simple-remote-complex vehicle control strategies are likely to unlock the potential of implementing methods and tools that are presently used only in an off-line setting. The cloud can also be used as a storage place to record current and historic vehicle data that can be used for predictive diagnosis and prognostics of the vehicle health.

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Vehicle posture control through aggressive maneuvering for mitigation of T-bone collisions

Cited by 16 publications

References 15 publications

Models and methodology for optimal trajectory generation in safety-critical road–vehicle manoeuvres

Models and methodology for optimal trajectory generation in safety-critical road–vehicle manoeuvres

An Investigation of Optimal Vehicle Maneuvers for Different Road Conditions

Future Mobility

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