Robust feature descriptor and vehicle motion model with tracking-by-detection for active safety

2013 ◽  
Author(s):  
Hirokatsu Kataoka ◽  
Kimimasa Tamura ◽  
Yoshimitsu Aoki ◽  
Yasuhiro Matsui ◽  
Kenji Iwata ◽  
...  
Keyword(s):  
Feature Descriptor ◽  
Active Safety ◽  
Motion Model ◽  
Vehicle Motion ◽  
Tracking By Detection

scholarly journals Extended Feature Descriptor and Vehicle Motion Model with Tracking-by-Detection for Pedestrian Active Safety

2014 ◽  
Vol E97.D (2) ◽  
pp. 296-304 ◽  
Author(s):  
Hirokatsu KATAOKA ◽  
Kimimasa TAMURA ◽  
Kenji IWATA ◽  
Yutaka SATOH ◽  
Yasuhiro MATSUI ◽  
...  
Keyword(s):  
Feature Descriptor ◽  
Active Safety ◽  
Motion Model ◽  
Vehicle Motion ◽  
Tracking By Detection

scholarly journals Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles

2018 ◽  
Vol 8 (12) ◽  
pp. 2632 ◽  
Author(s):  
Donghoon Shin ◽  
Kangmun Park ◽  
Manbok Park
Keyword(s):  
Risk Assessment ◽  
Active Safety ◽  
Vehicular Communication ◽  
Automated Driving ◽  
Drive Mode ◽  
V2v Communication ◽  
Vehicle To Vehicle ◽  
Safety Intervention ◽  
Vehicle Motion ◽  
Computer Simulation Studies

This paper proposes a human-centered risk assessment algorithm designed to find the intervention moment of drive mode and active safety mode while monitoring threat vehicles ahead to overcome effects of vehicular communication on risk assessment in automated driving vehicle. Although a conventional radar system is known to be best fitted on-board ranging sensor in terms of longitudinal safety, it is generally not enough for a reliable automated driving because of sensing uncertainty of the traffic environments and incomplete perception results due to sensor limitations. This can be overcome by implementing vehicle-to-vehicle (V2V) communication which provides complementary source of target vehicle’s dynamic behavior. Using V2V communication with vehicle internal and surround information obtained from the on-board sensor system, future vehicle motion has been predicted. With accurately predicted motion of a remote vehicle, a collision risk and the automated drive mode are determined by incorporating human factor. Effects of the V2V communication on a human-centered risk assessment algorithm have been investigated through a safe triangle analysis. The computer simulation studies have been conducted in order to validate the performance of the proposed algorithm. It has been shown that the V2V communication with the proposed risk assessment algorithm allows a faster drive mode decision and active safety intervention moment.

A New Vehicle Motion Model for Improved Predictions and Situation Assessment

2011 ◽  
Vol 12 (4) ◽  
pp. 1209-1219 ◽  
Author(s):  
Joakim Sorstedt ◽  
Lennart Svensson ◽  
Fredrik Sandblom ◽  
Lars Hammarstrand
Keyword(s):  
Motion Model ◽  
Situation Assessment ◽  
Vehicle Motion

scholarly journals Using synthetic basis feature descriptor for motion estimation

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141880383
Author(s):  
Dong Zhang ◽  
Alok Desai ◽  
Dah-Jye Lee
Keyword(s):  
Motion Estimation ◽  
Moving Objects ◽  
Feature Descriptor ◽  
Motion Field ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Slowing Down ◽  
Vision Sensors ◽  
Vehicle Motion ◽  
Assistance Systems

Development of advanced driver assistance systems has become an important focus for automotive industry in recent years. Within this field, many computer vision–related functions require motion estimation. This article discusses the implementation of a newly developed SYnthetic BAsis (SYBA) feature descriptor for matching feature points to generate a sparse motion field for analysis. Two motion estimation examples using this sparse motion field are presented. One uses motion classification for monitoring vehicle motion to detect abrupt movement and to provide a rough estimate of the depth of the scene in front of the vehicle. The other one detects moving objects for vehicle surrounding monitoring to detect vehicles with movements that could potentially cause collisions. This algorithm detects vehicles that are speeding up from behind, slowing down in the front, changing lane, or passing. Four videos are used to evaluate these algorithms. Experimental results verify SYnthetic BAsis’ performance and the feasibility of using the resulting sparse motion field in embedded vision sensors for motion-based driver assistance systems.

scholarly journals Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter

2020 ◽  
Vol 10 (4) ◽  
pp. 1343 ◽  
Author(s):  
Jianfeng Chen ◽  
Congcong Guo ◽  
Shulin Hu ◽  
Jiantian Sun ◽  
Reza Langari ◽  
...  
Keyword(s):  
Robust Estimation ◽  
Unscented Kalman Filter ◽  
Longitudinal Velocity ◽  
Gaussian White Noise ◽  
Active Safety ◽  
Safety Control ◽  
Guaranteed Estimation ◽  
Set Membership ◽  
Vehicle Motion ◽  
Vehicle Active Safety

Reliable vehicle motion states are critical for the precise control performed by vehicle active safety systems. This paper investigates a robust estimation strategy for vehicle motion states by feat of the application of the extended set-membership filter (ESMF). In this strategy, a system noise source is only limited as unknown but bounded, rather than the Gaussian white noise claimed in the stochastic filtering algorithms, such as the unscented Kalman filter (UKF). Moreover, as one part of this strategy, a calculation scheme with simple structure is proposed to acquire the longitudinal and lateral tire forces with acceptable accuracy. Numerical tests are carried out to verify the performance of the proposed strategy. The results indicate that as compared with the UKF-based one, it not only has higher accuracy, but also can provide a 100% hard boundary which contains the real values of the vehicle states, including the vehicle’s longitudinal velocity, lateral velocity, and sideslip angle. Therefore, the ESMF-based strategy can proffer a more guaranteed estimation with robustness for practical vehicle active safety control.

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