A DNN Based Driving Scheme for Anticipatory Car Following Using Road-Speed Profile

Author(s):  
Most. Kaniz Fatema Isha ◽  
Md. Nazirul Hasan Shawon ◽  
Md. Shamim ◽  
Md. Nazmus Shakib ◽  
M.M.A. Hashem ◽  
...  
2021 ◽  
Vol 12 (1) ◽  
pp. 6
Author(s):  
Alexander Koch ◽  
Tim Bürchner ◽  
Thomas Herrmann ◽  
Markus Lienkamp

Electrification and automatization may change the environmental impact of vehicles. Current eco-driving approaches for electric vehicles fit the electric power of the motor by quadratic functions and are limited to powertrains with one motor and single-speed transmission or use computationally expensive algorithms. This paper proposes an online nonlinear algorithm, which handles the non-convex power demand of electric motors. Therefore, this algorithm allows the simultaneous optimization of speed profile and powertrain operation for electric vehicles with multiple motors and multiple gears. We compare different powertrain topologies in a free-flow scenario and a car-following scenario. Dynamic Programming validates the proposed algorithm. Optimal speed profiles alter for different powertrain topologies. Powertrains with multiple gears and motors require less energy during eco-driving. Furthermore, the powertrain-dependent correlations between jerk restriction and energy consumption are shown.


2009 ◽  
Vol 42 (15) ◽  
pp. 320-327
Author(s):  
J. Daniel ◽  
G. Pouly ◽  
A. Birouche ◽  
J-P. Lauffenburger ◽  
M. Basset
Keyword(s):  

2013 ◽  
Vol 393 ◽  
pp. 982-987
Author(s):  
N.M. Hanif Zamakhshari ◽  
Ahmad Khushairy Makhtar ◽  
M. Hanif Ramli

ntelligent Speed Adaptation (ISA) is a system that constantly monitors vehicle speed, local speed limit on a road and implements an action such as giving warning or discourages the drivers when the vehicle is detected to be exceeding the speed limit. A GPS connected to digital speed map allows Intelligent Speed Adaptation technology to continuously update the vehicle speed limit to the road speed limit. The main purpose of this project is to study the speed profile and effect on drivers psychology on Intelligent Speed Adaptation to bus drivers before and after the intervention of ISA technology. An experiment was conducted on GPS-ISA instrument involving about 20 respondents of Universiti Teknologi MARA (UiTM) Shah Alam Campus bus drivers from various backgrounds. The instrument used to collect data is GPS-ISA device. The data gained from GPS-ISA device is speed profile to specify the speeding and speed variation. To evaluate drivers psychology, a set of questionnaire was designed. The data gained from questionnaire are attention level, stress level, and ISA acceptance level. The result of total differences for all 20 respondents between the average speed before and after the intervention for Zone 1 to Zone 5 was-8.95 km/h. For drivers psychology results, most of the respondents are willing to use ISA system if given a chance. Majority of respondents did not felt any stress and distraction while driving by using ISA system. For conclusion, the ISA system proved to be efficiently reduced speed of busses in UiTM Shah Alam campus zone and can be used as an initiative in order to assist bus drivers to reduce speed of vehicles especially in campus zone.


2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Jie Hu ◽  
Sheng Luo

The modeling of car-following behavior is an attractive research topic in traffic simulation and intelligent transportation. The driver plays an important role in car following but is ignored by most car-following models. This paper presents a novel car-following driver model, which can retain aspects of human driving styles. First, simulated car-following data are generated by using the speed control driver model and the real-world driving behavior data if the real-world car-following data are not available. Then, the car-following driver model is established by imitating human driving maneuver during real-world car following. This is accomplished by using a neural network-based learning control paradigm and car-following data. Finally, the FTP-72 driving cycle is borrowed as the speed profile of the leading vehicle for the model test. The driving style is quantitatively analyzed by AESD. The results show that the proposed car-following driver model is capable of retaining the naturalistic driving styles while well accomplishing the car-following task with the error of relative distance mostly less than 5 meters for every driving styles.


2003 ◽  
Author(s):  
Nicholas J. Ward ◽  
Michael P. Manser ◽  
Dick de Waard ◽  
Nobuyuki Kuge ◽  
Erwin Boer

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