Lining-Up Stabilizers for Pusher and Puller Articulated Vehicles

2020 ◽  
pp. 917-927
Author(s):  
Maciej Marcin Michałek
Keyword(s):  
Articulated Vehicles

scholarly journals Modelling and Stability Analysis of Articulated Vehicles

2021 ◽  
Vol 11 (8) ◽  
pp. 3663
Author(s):  
Tianlong Lei ◽  
Jixin Wang ◽  
Zongwei Yao
Keyword(s):  
Stability Analysis ◽  
Critical Velocity ◽  
Equations Of State ◽  
Steering System ◽  
Analysis Model ◽  
Nonlinear Dynamic Model ◽  
Equilibrium Equations ◽  
Eigenvalue Method ◽  
Articulated Vehicles ◽  
The Stability

This study constructs a nonlinear dynamic model of articulated vehicles and a model of hydraulic steering system. The equations of state required for nonlinear vehicle dynamics models, stability analysis models, and corresponding eigenvalue analysis are obtained by constructing Newtonian mechanical equilibrium equations. The objective and subjective causes of the snake oscillation and relevant indicators for evaluating snake instability are analysed using several vehicle state parameters. The influencing factors of vehicle stability and specific action mechanism of the corresponding factors are analysed by combining the eigenvalue method with multiple vehicle state parameters. The centre of mass position and hydraulic system have a more substantial influence on the stability of vehicles than the other parameters. Vehicles can be in a complex state of snaking and deviating. Different eigenvalues have varying effects on different forms of instability. The critical velocity of the linear stability analysis model obtained through the eigenvalue method is relatively lower than the critical velocity of the nonlinear model.

On the Backward Maneuverability of Articulated Vehicles

1985 ◽  
Vol 14 (1-3) ◽  
pp. 42-46 ◽  
Author(s):  
Ichiro KAGEYAMA ◽  
Yasushi SAITO
Keyword(s):  
Articulated Vehicles

scholarly journals A VR Truck Docking Simulator Platform for Developing Personalized Driver Assistance

2021 ◽  
Vol 11 (19) ◽  
pp. 8911
Author(s):  
Pedro Ribeiro ◽  
André Frank Krause ◽  
Phillipp Meesters ◽  
Karel Kural ◽  
Jason van Kolfschoten ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Truck Drivers ◽  
Driver Assistance ◽  
Future Directions ◽  
Driving Experience ◽  
Preliminary Results ◽  
Comprehensive Account ◽  
Articulated Vehicles ◽  
The Future ◽  
Vr Simulator

Professional truck drivers frequently face the challenging task of manually backwards manoeuvring articulated vehicles towards the loading bay. Logistics companies experience costs due to damage caused by vehicles performing this manoeuvre. However, driver assistance aimed to support drivers in this special scenario has not yet been clearly established. Additionally, to optimally improve the driving experience and the performance of the assisted drivers, the driver assistance must be able to continuously adapt to the needs and preferences of each driver. This paper presents the VISTA-Sim, a platform that uses a virtual reality (VR) simulator to develop and evaluate personalized driver assistance. This paper provides a comprehensive account of the VISTA-Sim, describing its development and main functionalities. The paper reports the usage of VISTA-Sim through the scenario of parking a semi-trailer truck in a loading bay, demonstrating how to learn from driver behaviours. Promising preliminary results indicate that this platform provides means to automatically learn from a driver’s performance. The evolution of this platform can offer ideal conditions for the development of ADAS systems that can automatically and continuously learn from and adapt to an individual driver. Therefore, future ADAS systems can be better accepted and trusted by drivers. Finally, this paper discusses the future directions concerning the improvement of the platform.

Rollover Warning of Articulated Vehicles Based on a Time-To-Rollover Metric

1999 ◽  
Author(s):  
Bo-Chiuan Chen ◽  
Huei Peng
Keyword(s):  
Real Time ◽  
The Other ◽  
Roll Angle ◽  
Vehicle Speed ◽  
Time Model ◽  
Whole Process ◽  
Articulated Vehicles ◽  
Articulated Vehicle ◽  
The One ◽  
Accuracy Problem

Abstract A Time-To-Rollover (TTR) metric is proposed as the basis to assess rollover threat for an articulated vehicle. Ideally, a TTR metric will accurately “count-down” toward rollover regardless of vehicle speed and steering patterns, so that the level of rollover threat is accurately indicated. To implement TTR in real-time, there are two conflicting requirements. On the one hand, a faster-than-real-time model is needed. On the other hand, the TTR predicted by this model needs to be accurate enough under all driving scenarios. An innovative approach is proposed in this paper to solve this dilemma and the whole process is illustrated in a design example. First, a simple yet reasonably accurate yaw/roll model is identified. A Neural Network (NN) is then developed to mitigate the accuracy problem of this simplified real-time model. The NN takes the TTR generated by the simplified model, vehicle roll angle and change of roll angle to generate an enhanced NN-TTR index. The NN was trained and verified under a variety of driving patterns. It was found that an accurate TTR is achievable across all the driving scenarios we tested.

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