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A Personalized Lane-Changing Model for Advanced Driver Assistance System Based on Deep Learning and Spatial-Temporal Modeling

SAE International Journal of Transportation Safety

Jianghan University, China-Jun Gao, Jiangang Yi
University of Michigan-Dearborn, USA-Yi Lu Murphey
  • Journal Article
  • 09-07-02-0009
Published 2019-11-14 by SAE International in United States
Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. However, modeling driver’s lane-changing decision and implementation process is challenging due to the complexity and uncertainty of driving behaviors. To address this issue, this article presents a personalized Lane-Changing Model (LCM) for Advanced Driver Assistance System (ADAS) based on deep learning method. The LCM contains three major computational components. Firstly, with abundant inputs of Root Residual Network (Root-ResNet), LCM is able to exploit more local information from the front view video data. Secondly, the LCM has an ability of learning the global spatial-temporal information via Temporal Modeling Blocks (TMBs). Finally, a two-layer Long Short-Term Memory (LSTM) network is used to learn video contextual features combined with lane boundary based distance features in lane change events. The experimental results on a -world driving dataset show that the LCM is capable of learning the latent features of lane-changing behaviors and achieving significantly better performance than other prevalent models.
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Detection of Lane-Changing Behavior Using Collaborative Representation Classifier-Based Sensor Fusion

SAE International Journal of Transportation Safety

University of Michigan-Dearborn, USA-Jun Gao, Yi Lu Murphey
Wuhan University of Technology, China-Honghui Zhu
  • Journal Article
  • 09-06-02-0010
Published 2018-10-29 by SAE International in United States
Sideswipe accidents occur primarily when drivers attempt an improper lane change, drift out of lane, or the vehicle loses lateral traction. In this article, a fusion approach is introduced that utilizes data from two differing modality sensors (a front-view camera and an onboard diagnostics (OBD) sensor) for the purpose of detecting driver’s behavior of lane changing. For lane change detection, both feature-level fusion and decision-level fusion are examined by using a collaborative representation classifier (CRC). Computationally efficient detection features are extracted from distances to the detected lane boundaries and vehicle dynamics signals. In the feature-level fusion, features generated from two differing modality sensors are merged before classification, while in the decision-level fusion, the Dempster-Shafer (D-S) theory is used to combine the classification outcomes from two classifiers, each corresponding to one sensor. The results indicated that the feature-level fusion outperformed the decision-level fusion, and the introduced fusion approach using a CRC performs significantly better in terms of detection accuracy, in comparison to other state-of-the-art classifiers.
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Full Vehicle Dynamic Modeling for Engine Shake with Hydraulic Engine Mount

Dongfeng Motor Co.,Ltd.-Zhao-ming Wu, Shao-kang Zhang
Tongji University-Rong Guo, Jun Gao, Xiao-kang Wei
Published 2017-06-05 by SAE International in United States
The statement of the engine shake problem is presented through comparing the quarter vehicle models with the rigid-connected and flexible-connected powertrain which is supported on the body by a rubber mount. Then the model is extended by replacing the rubber mount as a hydraulic engine mount (HEM) with regard to the inertia and resistance of the fluid within the inertia track. Based on these, a full vehicle model with 14 degree of freedoms (DOFs) is proposed to calculate the engine shake, which consists of 6 of the powertrain, 1 of the fluid within the inertia track of the HEM, 3 of the car body and 4 of the unsprung mass. Simulation analysis based on the proposed model is implemented, through which the conclusion is drawn that the HEM has great influence on the body and seat track response subjected to front wheel inputs, compared with the rubber mount.
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An Improved PID Controller Based on Particle Swarm Optimization for Active Control Engine Mount

Tongji University-Rong Guo, Xiao-Kang Wei, Jun Gao
Published 2017-03-28 by SAE International in United States
Manufacturers have been encouraged to accommodate advanced downsizing technologies such as the Variable Displacement Engine (VDE) to satisfy commercial demands of comfort and stringent fuel economy. Particularly, Active control engine mounts (ACMs) notably contribute to ensuring superior effectiveness in vibration attenuation. This paper incorporates a PID controller into the active control engine mount system to attenuate the transmitted force to the body. Furthermore, integrated time absolute error (ITAE) of the transmitted force is introduced to serve as the control goal for searching better PID parameters. Then the particle swarm optimization (PSO) algorithm is adopted for the first time to optimize the PID parameters in the ACM system. Simulation results are presented for searching optimal PID parameters. In the end, experimental validation is conducted to verify the optimized PID controller. The study demonstrates that the improved PID controller reveals effective vibration isolation performance.
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A Method of Acceleration Order Extraction for Active Engine Mount

Tongji University-Rong Guo, Jun Gao, Xiao-kang Wei
Published 2017-03-28 by SAE International in United States
The active engine mount (AEM) is developed in automotive industry to improve overall NVH performance. The AEM is designed to reduce major-order signals of engine vibration over a broad frequency range, therefore it is of vital importance to extract major-order signals from vibration before the actuator of the AEM works. This work focuses on a method of real-time extraction of the major-order acceleration signals at the passive side of the AEM. Firstly, the transient engine speed is tracked and calculated, from which the FFT method with a constant sampling rate is used to identify the time-related frequencies as the fundamental frequencies. Then the major-order signals in frequency domain are computed according to the certain multiple relation of the fundamental frequencies. After that, the major-order signals can be reconstructed in time domain, which are proved accurate through offline simulation, compared with the given signals. To verify the real-time performance of the method, a hardware-in-the-loop testing system based on MATLAB xPC target is established. LMS Data Acquisition System is adopted to track rotating speed online and extract…
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