Deriving system parameters of a 360° low speed autonomous emergency braking driver assistance system for parking and maneuvering based on naturalistic driving studies

Feig, Philip; König, Adrian; Gschwendtner, Klaus; Lohrer, Jurgen; Schatz, Julian; Lienkamp, Markus

doi:10.1109/icves.2017.7991918

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…Recently, parking assistance systems (PAS) have also been developed to accurately identify obstacles and to park a car automatically using sensor fusion [ 8 ]. Autonomous emergency braking (AEB) has also been developed to cope with emergencies that are difficult for drivers to handle [ 9 ]. Adaptive cruise control (ACC) was first proposed in the 1960s to assist drivers and, more recently, to take into account not only convenience and safety but also fuel economy to make optimal driving possible [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors

Park

Lee

et al. 2018

Sensors

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Vehicle control systems such as ESC (electronic stability control), MDPS (motor-driven power steering), and ECS (electronically controlled suspension) improve vehicle stability, driver comfort, and safety. Vehicle control systems such as ACC (adaptive cruise control), LKA (lane-keeping assistance), and AEB (autonomous emergency braking) have also been actively studied in recent years as functions that assist drivers to a higher level. These DASs (driver assistance systems) are implemented using vehicle sensors that observe vehicle status and send signals to the ECU (electronic control unit). Therefore, the failure of each system sensor affects the function of the system, which not only causes discomfort to the driver but also increases the risk of accidents. In this paper, we propose a new method to detect and isolate faults in a vehicle control system. The proposed method calculates the constraints and residuals of 12 systems by applying the model-based fault diagnosis method to the sensor of the chassis system. To solve the inaccuracy in detecting and isolating sensor failure, we applied residual sensitivity to a threshold that determines whether faults occur. Moreover, we applied a sensitivity analysis to the parameters semi-correlation table to derive a fault isolation table. To validate the FDI (fault detection and isolation) algorithm developed in this study, fault signals were injected and verified in the HILS (hardware-in-the-loop simulation) environment using an RCP (rapid control prototyping) device.

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Section: Introductionmentioning

confidence: 99%