A pre-crash discrimination system for an airbag deployment algorithm

Cho, Kwang-Hyun; Choi, Seibum B.; Shin, Kyung‐Jae; Yun, Yousik

doi:10.1109/acc.2010.5531329

Cited by 3 publications

(2 citation statements)

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“…The cornering stiffness for the front and rear tires was obtained as the linear slope at the origin of the lateral force versus slip curves [40] from the CarSim 225/16 R18 Touring Tire data [34]. The lateral tire forces were approximated as linear using tire cornering stiffness (C α ) and lateral tire slip angle (α) with F y = C α α, in accordance with the literature [39][40][41][42][43][44].…”

Section: Vehicle and Trajectory Modelingmentioning

confidence: 99%

“…• Through AV actuator (steering, throttle, brake) control, perform the safest emergency maneuver based on environmental, AV, and ARO states; • Provide warnings to the vehicle occupants based on AV and ARO states and, potentially, before the EOAM begins. The warnings can also be used to activate pre-collision safety measures, such as occupant safety belt pre-tensioners, airbag arming, and increased seat side and leg bolstering [33,34]. This EOAM framework should also embrace operational design domain (ODD) and dynamic driving task (DDT)-based architecture [28][29][30][31] that allows for specific domains to take care of unique tasks that occur less frequently than normal driving conditions; one of the domains could manage emergency maneuvers involving large-magnitude lateral dynamics with low processing and execution times.…”

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confidence: 99%

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Autonomous Vehicle Emergency Obstacle Avoidance Maneuver Framework at Highway Speeds

Lowe,

Guvenc

2023

Electronics

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An autonomous vehicle (AV) uses high-level decision making and lower-level actuator controls, such as throttle (acceleration), braking (deceleration), and steering (change in lateral direction) to navigate through various types of road networks. Path planning and path following for highway driving are currently available in series-produced highly automated vehicles. In addition to these, emergency collision avoidance decision making and maneuvering are another key and essential feature that is needed in a series production AV at highway driving speeds. For reliability, low cost, and fast computation, such an emergency obstacle avoidance maneuvering system should use well-established conventional methods as opposed to data-driven neural networks or reinforcement learning methods, which are currently not suitable for use in highway AV driving. This paper presents a novel Emergency Obstacle Avoidance Maneuver (EOAM) methodology for AVs traveling at higher speeds and lower road surface friction, involving time-critical maneuver determination and control. The proposed EOAM framework offers usage of the AV’s sensing, perception, control, and actuation system abilities as one cohesive system to avoid an on-road obstacle, based first on performance feasibility and second on passenger comfort, and it is designed to be well integrated within an AV’s high-level control and decision-making system. To demonstrate the efficacy of the proposed method, co-simulation including the AV’s EOAM logic in Simulink and a vehicle model in CarSim is conducted with speeds ranging from 55 to 165 km/h and on road surfaces with friction ranging from 1.0 to 0.1. The results are analyzed and interpreted in the context of an entire AV system, with implications for future work.

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