scholarly journals Lane change maneuver for autonomous vehicles (Benchmark Proposal)

10.29007/5hxt ◽  
2018 ◽  
Author(s):  
Nikolaos Kekatos ◽  
Daniel Heß ◽  
Goran Frehse
Keyword(s):  
Autonomous Vehicles ◽  
Lane Change ◽  
Lane Changes ◽  
Cooperative Driving ◽  
Bicycle Model ◽  
Moving Vehicles ◽  
Control Scheme ◽  
Lateral Behavior ◽  
The Right ◽  
Left Lane

Lane changes are known to be risky maneuvers both for autonomous vehicles and human drivers since they require changes in longitudinal and lateral velocities in the presence of other moving vehicles. In this paper, we propose a benchmark modeling a cooperative lane change maneuver that involves four fully autonomous vehicles; three in the left lane and one in the right. The vehicle driving in the right lane aims to move to the left lane while avoiding a collision with the other vehicles. Each vehicle is equipped with sensors and can also communicate with its neighboring vehicles. The vehicle dynamics are described by a dynamic bicycle model and each vehicle is equipped with a linear low-level controller that regulates its own longitudinal and lateral behavior. To guarantee that the maneuver is safe and the traffic rules are enforced, we employ a cooperative driving control scheme (in the spirit of supervisory logic) that decides the actions of each vehicle.

Characterizing Lane Changes via Digitized Infrastructure and Low-Cost GPS

2019 ◽  
Vol 2673 (8) ◽  
pp. 298-309 ◽  
Author(s):  
Ishtiak Ahmed ◽  
Alan Karr ◽  
Nagui M. Rouphail ◽  
Gyounghoon Chun ◽  
Shams Tanvir
Keyword(s):  
Autonomous Vehicles ◽  
Lateral Displacement ◽  
Low Cost ◽  
Detection Algorithm ◽  
Lane Change ◽  
Trajectory Data ◽  
Lane Changing ◽  
Lane Changes ◽  
Detection Systems ◽  
Level Information

With the expected increase in the availability of trajectory-level information from connected and autonomous vehicles, issues of lane changing behavior that were difficult to assess with traditional freeway detection systems can now begin to be addressed. This study presents the development and application of a lane change detection algorithm that uses trajectory data from a low-cost GPS-equipped fleet, supplemented with digitized lane markings. The proposed algorithm minimizes the effect of GPS errors by constraining the temporal duration and lateral displacement of a lane change detected using preliminary lane positioning. The algorithm was applied to 637 naturalistic trajectories traversing a long weaving segment and validated through a series of controlled lane change experiments. Analysis of naturalistic trajectory data revealed that ramp-to-freeway trips had the highest number of discretionary lane changes in excess of 1 lane change/vehicle. Overall, excessive lane change rates were highest between the two middle freeway lanes at 0.86 lane changes/vehicle. These results indicate that extreme lane changing behavior may significantly contribute to the peak-hour congestion at the site. The average lateral speed during lane change was 2.7 fps, consistent with the literature, with several freeway–freeway and ramp–ramp trajectories showing speeds up to 7.7 fps. All ramp-to-freeway vehicles executed their first mandatory lane change within 62.5% of the total weaving length, although other weaving lane changes were spread over the entire segment. These findings can be useful for implementing strategies to lessen abrupt and excessive lane changes through better lane pre-positioning.

scholarly journals Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2259 ◽  
Author(s):  
Chang Wang ◽  
Qinyu Sun ◽  
Zhen Li ◽  
Hongjia Zhang
Keyword(s):  
Risk Perception ◽  
Autonomous Vehicles ◽  
Decision Model ◽  
Signal Detection Theory ◽  
Critical Issue ◽  
Lane Change ◽  
Mixed Traffic ◽  
Driving System ◽  
Lane Changes ◽  
The Subject

Determining an appropriate time to execute a lane change is a critical issue for the development of Autonomous Vehicles (AVs).However, few studies have considered the rear and the front vehicle-driver’s risk perception while developing a human-like lane-change decision model. This paper aims to develop a lane-change decision model for AVs and to identify a two level threshold that conforms to a driver’s perception of the ability to safely change lanes with a rear vehicle approaching fast. Based on the signal detection theory and extreme moment trials on a real highway, two thresholds of safe lane change were determined with consideration of risk perception of the rear and the subject vehicle drivers, respectively. The rear vehicle’s Minimum Safe Deceleration (MSD) during the lane change maneuver of the subject vehicle was selected as the lane change safety indicator, and was calculated using the proposed human-like lane-change decision model. The results showed that, compared with the driver in the front extreme moment trial, the driver in the rear extreme moment trial is more conservative during the lane change process. To meet the safety expectations of the subject and rear vehicle drivers, the primary and secondary safe thresholds were determined to be 0.85 m/s2 and 1.76 m/s2, respectively. The decision model can help make AVs safer and more polite during lane changes, as it not only improves acceptance of the intelligent driving system, but also further ensures the rear vehicle’s driver’s safety.

Effect of connected and autonomous vehicles on traffic flow at a bidirectional road

2021 ◽  
Author(s):  
H. Echab ◽  
A. Khallouk ◽  
H. Ez-Zahraouy
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Negative Influence ◽  
Conventional Vehicle ◽  
Cooperative Driving ◽  
Moving Vehicles ◽  
Penetration Ratio ◽  
Slow Moving ◽  
The Impact ◽  
Cv Model

The objective of this study was to investigate the impact of connected and autonomous vehicles (CAVs) on traffic flow under various parameters. For this purpose, we propose a mixed CAV and conventional vehicle (CV) model to investigate a bidirectional two-lane traffic flow under the periodic boundary condition. The traffic flux and the phase diagrams of the system in the ([Formula: see text]) area are constructed in both cases: with and without CAVs. The overtaking frequency is also calculated. The simulation findings show that the traffic capacity is greatly enhanced with the increase in the CAV penetration ratio. Owing to the cooperative driving strategy, with the increase in penetration ratio of the CAV, the portion of smooth overtaking is boosted. Furthermore, it is found that the traffic throughput is positively correlated to the speed limit of the fast vehicle where the flux increases as [Formula: see text] increases. Also, even if there is a low rate of slow moving vehicles in the system, it will have an appreciable and a significant negative influence.

Automating lane changes and collision avoidance on highways via distributed agreement

2019 ◽  
Vol 67 (12) ◽  
pp. 1047-1057
Author(s):  
Fabio Molinari ◽  
Aaron Grapentin ◽  
Alexandros Charalampidis ◽  
Jörg Raisch
Keyword(s):  
Control Strategy ◽  
Autonomous Vehicles ◽  
Optimal Trajectory ◽  
Hierarchical Control ◽  
Lane Changes ◽  
The Road ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Control Scheme ◽  
On The Road

Abstract This work presents a distributed hierarchical control strategy for fleets of autonomous vehicles cruising on a highway with diverse desired speeds. The goal is to design a control scheme that can be employed in scenarios where only vehicle-to-vehicle communication is available and where vehicles need to negotiate and agree on their positions on the road. To this end, after reaching an agreement on the lane speed with other traffic participants, each vehicle decides whether to keep cruising along the current lane or to move into another one. In the latter case, it negotiates the entry point with others by taking part in a distributed auction. An onboard controller computes an optimal trajectory transferring the vehicle with agreed velocity to the desired lane while avoiding collisions.

Investigation of the Effect of Super Sharrows on Cyclist and Vehicle Behavior

2017 ◽  
Vol 2659 (1) ◽  
pp. 224-232 ◽  
Author(s):  
Ali Kassim ◽  
Karim Ismail ◽  
Suzanne Woo
Keyword(s):  
Statistical Analysis ◽  
Motor Vehicle ◽  
Potential Effect ◽  
Safety Performance ◽  
Motor Vehicles ◽  
Lane Change ◽  
Performance Parameters ◽  
The Right ◽  
Left Lane

This study examined the potential effect of special paintings of shared lane markings (super sharrows) on a number of operational and safety performance parameters for cyclists and motor vehicles. These performance parameters were used to assess pretreatment and posttreatment behavior when cyclists and motor vehicles were near one another. The performance parameters were ( a) rate of lane change maneuvers performed by vehicles in the presence as well as the absence of cyclists and ( b) lateral spacing between cyclists, vehicles, and curb edge. In general, the main objectives of this treatment were ( a) providing cyclists with comfort by allowing them to ride in the middle of the travel lane and ( b) promoting safe passing by motor vehicles. The effect of the super sharrows on cyclists and motor vehicles was analyzed with statistical analysis by comparing pretreatment and posttreatment conditions. The key findings are as follow: ( a) super sharrows had an effect on motor vehicle lane change maneuvers, represented by an increase in the percentage of motor vehicles that changed from the right lane (location of super sharrows) to the left lane with the presence of a cyclist on the right lane; ( b) the number of motor vehicles that changed from right lane to left lane and back to right lane in both full and partial encroachment into the left lane decreased; ( c) the number of the motor vehicle lane change maneuvers from left to right lane decreased; and ( d) cyclists were found to be riding farther from the right curb with the presence of the super sharrows.

Design and implementation of human driving data–based active lane change control for autonomous vehicles

2020 ◽  
Vol 235 (1) ◽  
pp. 55-77
Author(s):  
Heungseok Chae ◽  
Yonghwan Jeong ◽  
Hojun Lee ◽  
Jongcherl Park ◽  
Kyongsu Yi
Keyword(s):  
Real World ◽  
Autonomous Vehicles ◽  
High Speed ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Lane Change ◽  
Driving Mode ◽  
Lane Changes ◽  
Safety Indices

This article describes the design, implementation, and evaluation of an active lane change control algorithm for autonomous vehicles with human factor considerations. Lane changes need to be performed considering both driver acceptance and safety with surrounding vehicles. Therefore, autonomous driving systems need to be designed based on an analysis of human driving behavior. In this article, manual driving characteristics are investigated using real-world driving test data. In lane change situations, interactions with surrounding vehicles were mainly investigated. And safety indices were developed with kinematic analysis. A safety indices–based lane change decision and control algorithm has been developed. In order to improve safety, stochastic predictions of both the ego vehicle and surrounding vehicles have been conducted with consideration of sensor noise and model uncertainties. The desired driving mode is decided to cope with all lane changes on highway. To obtain desired reference and constraints, motion planning for lane changes has been designed taking stochastic prediction-based safety indices into account. A stochastic model predictive control with constraints has been adopted to determine vehicle control inputs: the steering angle and the longitudinal acceleration. The proposed active lane change algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable lane changes in high-speed driving on highways have been demonstrated using our autonomous test vehicle.

Emergency Lane-Change Maneuvers of Autonomous Vehicles

1998 ◽  
Vol 120 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Zvi Shiller ◽  
Satish Sundar
Keyword(s):  
Autonomous Vehicles ◽  
Minimum Distance ◽  
Phase Plane ◽  
Lane Change ◽  
Initial Speed ◽  
Mass Model ◽  
Clearance Curve ◽  
Bicycle Model ◽  
Straight Line ◽  
Longitudinal Distance

This paper addresses the issue of collision avoidance using lane-change maneuvers. Of particular interest is to determine the minimum distance beyond which an obstacle cannot be avoided at a given initial speed. Using a planar bicycle model, we first compute the sharpest dynamically feasible maneuver by minimizing the longitudinal distance of a lane transition, assuming given initial and free final speeds. The minimum distance to an obstacle is then determined from the path traced by the optimal maneuver. Plotting the minimum distance in the phase plane establishes the clearance curve, a valuable tool for planning emergency maneuvers. For the bicycle model, the clearance curve is shown to closely correlate with the straight line produced by a point mass model. Examples demonstrate the use of the clearance curve for planning safe avoidance maneuvers.

Eye Glance Behavior of van and Passenger Car Drivers during Lane Change Decision Phase

2005 ◽  
Vol 1937 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Louis Tijerina ◽  
W. Riley Garrott ◽  
Duane Stoltzfus ◽  
Edwin Parmer
Keyword(s):  
Warning System ◽  
Lane Change ◽  
Passenger Cars ◽  
Passenger Car ◽  
Lane Changes ◽  
City Street ◽  
Test Route ◽  
Mirror Use ◽  
Instrumented Vehicle ◽  
The Right

Data are presented on the eye glance behavior of passenger car and van drivers before the start of discretionary lane changes. Thirty-nine volunteers ranging from 20 to 60 years of age served as either van drivers (N = 19) or passenger car drivers (N = 20) in the study. Each driver used an instrumented vehicle and was accompanied by a ride-along observer in daylight and dry pavement conditions. The test route included driving on both public highways at 55 mph or more and city roads at 25 to 35 mph. A total of 549 lane changes (290 for vans, 259 for passenger cars) were analyzed in terms of driver eye glance behavior 10 s before the lane change start. Results indicated that for left-to-right lane changes, the probability of a glance to the center mirror was substantially higher than the probability of a glance to the right side mirror. For right-to-left lane changes, the probability of a glance to the center mirror was substantially less than that for rightward lane changes, and the probability of a glance to the left side mirror was appreciably higher than that for right side mirror use in rightward lane changes. These results held for both van and passenger car drivers. Except for a slightly higher probability of over-the-shoulder glances on city roads, these results hold for both highway and city street driving. These data should be factored into the design of lane change warning system displays and mirror systems.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

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