3101 Development of Driving Behavior Evaluation Technique during Lane Change

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.

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Effects of Marking Automated Vehicles on Human Drivers on Highways

Information ◽

10.3390/info11060286 ◽

2020 ◽

Vol 11 (6) ◽

pp. 286 ◽

Cited By ~ 2

Author(s):

Tanja Fuest ◽

Alexander Feierle ◽

Elisabeth Schmidt ◽

Klaus Bengler

Keyword(s):

Road Traffic ◽

Driving Behavior ◽

Sensor Technology ◽

Lane Change ◽

Objective Data ◽

Automated Vehicles ◽

Traffic Jam ◽

Automated Vehicle ◽

Time Headway ◽

First Time

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

2011 IEEE International Conference on Information and Automation ◽

10.1109/icinfa.2011.5949037 ◽

2011 ◽

Author(s):

Guoqing Xu ◽

Li Liu ◽

Zhangjun Song ◽

Yongsheng Ou

Keyword(s):

Dynamic Model ◽

Driving Behavior ◽

Lane Change ◽

Change Trajectories

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Edge-based Lane Change Detection and its Application to Suspicious Driving Behavior Analysis

10.1109/iihmsp.2007.4457737 ◽

2007 ◽

Cited By ~ 4

Author(s):

Sin-Yu Chen ◽

Jun-Wei Hsieh

Keyword(s):

Change Detection ◽

Behavior Analysis ◽

Driving Behavior ◽

Lane Change ◽

Edge Based

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Driving-behavior-oriented trajectory planning for autonomous vehicle driving on urban structural road

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407020969992 ◽

2020 ◽

pp. 095440702096999

Author(s):

Dequan Zeng ◽

Zhuoping Yu ◽

Lu Xiong ◽

Junqiao Zhao ◽

Peizhi Zhang ◽

...

Keyword(s):

Path Planning ◽

Velocity Profile ◽

Success Rate ◽

Trajectory Planning ◽

Autonomous Vehicle ◽

Driving Behavior ◽

Vehicle Stability ◽

Lane Change ◽

Actuator Constraints ◽

Traffic Rules

A novel driving-behavior-oriented method is proposed in this paper for improving trajectory planning performance of autonomous vehicle driving on urban structural road. Differ from the irregularity and unpredictability of escaping a maze or travelling on off-road, the driving on road emphasizes more on the compliance of road traffic rules and the satisfaction of passenger comfort rather than purely pursuing the shortest route or the shortest time. Therefore, the driving-behavior-oriented framework is employed in trajectory planning, which divides trajectory into lane change, turn and U-turn, according to the basic traffic rules and the daily behaviors of drivers. The presented approach mainly includes basic path planning, fast-bias RRT path planning and velocity planning. The basic path planning consists of lane change, turn and U-turn behaviors, which generates smooth path with continuous curvature. In order to ensure the completeness of the programming algorithm, a fast-bias RRT (FB-RRT) algorithm is embedded. As guiding by the driving behavior, normal random, goal-bias and Gaussian sampling strategies are fused to form FB-RRT, which could make the best use of the basic path planning and reduce the randomness of node’s extension to save the computation time. After collision-free path generating, cubic polynomial curve is employed to schedule velocity profile for coping with vehicle stability requirements, actuator constraints and comfort conditions. The planner has been tested in simulation and a real vehicle in various typical scenarios. Test results illustrate that the presented method could generate a trajectory with controllable extrema of curvature as well as with continuous and smooth enough curvature. Besides, generated trajectory has short length, high success rate (no less than 80% average success rate in complex environment) and real time (the average period is less than 100 ms). Moreover, the velocity profile meets the vehicle stability requirements, actuator constraints, and comfort conditions.

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