Toward Next Active Safety Technology of Intelligent Vehicle

Yoshida, Hidehisa; Omae, Manabu; Wada, Takahiro

doi:10.20965/jrm.2015.p0610

Cited by 19 publications

(8 citation statements)

References 2 publications

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“…driver fatigue, and sideslip caused by low adhesion pavement under severe weather conditions [1]- [5]. With the advent of the era of automatic driving, intelligent vehicles can replace humans partially or completely in driving tasks, which has far-reaching significance in improving road traffic safety and protecting human life and property [6], [7]. Trajectory tracking control is a key technology in the research and development of autonomous vehicles.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control Algorithm for Autonomous Vehicle Considering Cornering Characteristics

et al. 2020

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Trajectory tracking control is a key technology in the research and development of autonomous vehicles. With the aim of addressing problems such as low control accuracy and poor real-time performance, which can occur easily when an autonomous vehicle avoids obstacles, this research focuses on the trajectory tracking control algorithm for autonomous vehicle considering cornering characteristics. First, the vehicle dynamics model and tire model are established through appropriate simplification. Then, based on the basic principle of model predictive control, a linear time-varying model predictive controller (LTV MPC) that considers the cornering characteristics is designed and optimized. Finally, using CarSim and MATLAB/Simulink software, a joint simulation model is established and the trajectory tracking performance of the controlled vehicle under different vehicle speeds and road adhesion conditions are tested through simulation experiments in combination with the double-shift line reference trajectory. The simulation results show the LTV MPC controller that considers cornering characteristics has good self-adaptability under complicated and severe working conditions, and no cases, such as car sideslip or track departure, were observed. Compared with other controllers and algorithms, the designed trajectory tracking controller has remarkable comprehensive performance, exhibits superior robustness and anti-interference ability, and significant improvements in the trajectory tracking control accuracy and real-time performance. The proposed control algorithm is of great importance in improving the tracking stability and driving safety of autonomous vehicles under complex extreme conditions and conducive to the further development and improvement of the technological level of intelligent vehicle driving assistance. INDEX TERMS Driving assistance, autonomous vehicle, model predictive control, cornering characteristics, trajectory tracking control.

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Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control Algorithm for Autonomous Vehicle Considering Cornering Characteristics

et al. 2020

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“…Due to lane departure warning limitations from environmental conditions that affect the performance of identifying the correct lanes [12], a new framework development on lane departure estimation is required to enhance further the system robustness in dealing with the current challenges. For example, there is a particular sensory system that has already existed on the vehicle, which can be used with the meagre cost such as a wheel speed sensor [11] in assisting lane departure warning application.…”

Section: Introductionmentioning

confidence: 99%

Lane Departure Warning Estimation Using Yaw Acceleration

2020

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Lane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simulated vehicle dynamic responses, the yaw acceleration response is observed to provide earlier insight in predicting the future lane departure event compared to other vehicle dynamics responses. The proposed model-based vehicle dynamics framework had shown the effectiveness in estimating lane departure using steering wheel angle and vehicle speed inputs.

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“…Traffic safety issues are largely caused by subjective reasons related to the driver, such as inattention, improper driving operation and non-compliance with traffic rules, and smart cars have become an effective means to eliminate these human factors [1,2,3,4,5]. Self-driving technology can assist, or even independently complete the driving operation, which is of remarkable importance to liberate the human body and considerably reduce the incidence of accidents [6,7]. Traffic sign detection and recognition are crucial in the development of intelligent vehicles, which directly affects the implementation of driving behaviors.…”

Section: Introductionmentioning

confidence: 99%

Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles

Cao

Song

Peng

et al. 2019

Sensors

102

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Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.

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Toward Next Active Safety Technology of Intelligent Vehicle

Cited by 19 publications

References 2 publications

Trajectory Tracking Control Algorithm for Autonomous Vehicle Considering Cornering Characteristics

Trajectory Tracking Control Algorithm for Autonomous Vehicle Considering Cornering Characteristics

Lane Departure Warning Estimation Using Yaw Acceleration

Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles

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