Generating lane-change trajectories using the dynamic model of driving behavior

Author(s):  
Guoqing Xu ◽  
Li Liu ◽  
Zhangjun Song ◽  
Yongsheng Ou
2018 ◽  
Vol 3 (3) ◽  
pp. 242-253 ◽  
Author(s):  
Ekim Yurtsever ◽  
Suguru Yamazaki ◽  
Chiyomi Miyajima ◽  
Kazuya Takeda ◽  
Masataka Mori ◽  
...  

1997 ◽  
Author(s):  
Nathaniel H. Sledge ◽  
Kurt M. Marshek

Author(s):  
Fabian Fontana ◽  
Jens Neubeck ◽  
Andreas Wagner ◽  
Jochen Wiedemann ◽  
Uli Schaaf ◽  
...  

2021 ◽  
Author(s):  
Xiaotong Yan ◽  
Jia Lee ◽  
Lingqiu Zeng ◽  
Yunni Xia
Keyword(s):  

2010 ◽  
Vol 2010.19 (0) ◽  
pp. 257-260
Author(s):  
Satoshi KUBO ◽  
Kozo MAEDA ◽  
Hideki TSUNAI ◽  
Ryuzo HAYASHI ◽  
Masao NAGAI ◽  
...  

2013 ◽  
Vol 671-674 ◽  
pp. 2843-2846 ◽  
Author(s):  
Chang Wang ◽  
Chu Qing Zheng

Aiming at the trajectory planning problem of intelligent vehicle during lane change process, 7 polynomials lane change model was used to control vehicle. Basic model of this model was established at first, and then lane change trajectories were solved by using restriction of movement state. At last, the commonly form of lane change trajectories were obtained. Using real road duration time of lane change, lane change trajectories were simulated with MATLAB. The results shows that this model was suitable for lane change trajectories planning in different speed and it can be used for intelligent vehicle controlling.


2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ehsan Ramezani-Khansari ◽  
Masoud Tabibi ◽  
Fereidoon Moghadas Nejad

Lane change (LC) is one of the main maneuvers in traffic flow. Many studies have estimated LC duration directly by using lane-based data. The current research presents an estimate of LC duration for overtaking maneuver in nonlane-based traffic flow. In this paper, the LC duration is estimated implicitly by modeling lateral speed and applying the length of required lateral movement to complete the LC maneuver. In lateral speed modeling, the local linear model tree is applied which consists of three variables: the initial lateral distance, longitudinal speed, and time to collision (TTC), which itself is a function of the relative speed of follower and the distance between the two vehicles. The initial lateral distance is the relative transverse distance from which the following vehicle initializes the LC. The range of lateral speed was estimated between 0.5 and 5 km/h, which resulted in the LC duration between 2.5 and 24 sec. The results indicate that the lateral and longitudinal speed would be inversely related, while the lateral speed and the initial transverse distance as well as TTC would be directly related. The findings also indicate that TTC can be assumed as the most important factor affecting lateral speed. TTC at 8 sec can be considered as the threshold for its effect on the LC duration since at longer TTCs, and the lateral speed has remained almost constant. When TTC is longer than 8 sec, it would not affect the LC duration.


Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 286 ◽  
Author(s):  
Tanja Fuest ◽  
Alexander Feierle ◽  
Elisabeth Schmidt ◽  
Klaus Bengler

Due to the short range of the sensor technology used in automated vehicles, we assume that the implemented driving strategies may initially differ from those of human drivers. Nevertheless, automated vehicles must be able to move safely through manual road traffic. Initially, they will behave as carefully as human learners do. In the same way that driving-school vehicles tend to be marked in Germany, markings for automated vehicles could also prove advantageous. To this end, a simulation study with 40 participants was conducted. All participants experienced three different highway scenarios, each with and without a marked automated vehicle. One scenario was based around some roadworks, the next scenario was a traffic jam, and the last scenario involved a lane change. Common to all scenarios was that the automated vehicles strictly adhered to German highway regulations, and therefore moved in road traffic somewhat differently to human drivers. After each trial, we asked participants to rate how appropriate and disturbing the automated vehicle’s driving behavior was. We also measured objective data, such as the time of a lane change and the time headway. The results show no differences for the subjective and objective data regarding the marking of an automated vehicle. Reasons for this might be that the driving behavior itself is sufficiently informative for humans to recognize an automated vehicle. In addition, participants experienced the automated vehicle’s driving behavior for the first time, and it is reasonable to assume that an adjustment of the humans’ driving behavior would take place in the event of repeated encounters.


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