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.

Modeling Integrated Lane-Changing Behavior

2003 ◽  
Vol 1857 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Tomer Toledo ◽  
Haris N. Koutsopoulos ◽  
Moshe E. Ben-Akiva
Keyword(s):  
Empirical Work ◽  
Flow Characteristics ◽  
Lane Change ◽  
Trajectory Data ◽  
Lane Changing ◽  
Simulation Tools ◽  
Lane Changes ◽  
Trade Offs ◽  
Safety Studies ◽  
Vehicle Trajectory

The lane-changing model is an important component within microscopic traffic simulation tools. Following the emergence of these tools in recent years, interest in the development of more reliable lane-changing models has increased. Lane-changing behavior is also important in several other applications such as capacity analysis and safety studies. Lane-changing behavior is usually modeled in two steps: ( a) the decision to consider a lane change, and ( b) the decision to execute the lane change. In most models, lane changes are classified as either mandatory (MLC) or discretionary (DLC). MLC are performed when the driver must leave the current lane. DLC are performed to improve driving conditions. Gap acceptance models are used to model the execution of lane changes. The classification of lane changes as either mandatory or discretionary prohibits capturing trade-offs between these considerations. The result is a rigid behavioral structure that does not permit, for example, overtaking when mandatory considerations are active. Using these models within a microsimulator may result in unrealistic traffic flow characteristics. In addition, little empirical work has been done to rigorously estimate the parameters of lane-changing models. An integrated lane-changing model, which allows drivers to jointly consider mandatory and discretionary considerations, is presented. Parameters of the model are estimated with detailed vehicle trajectory data.

scholarly journals A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Traffic ◽  
Road User ◽  
Lane Change ◽  
Traffic Light ◽  
User Interactions ◽  
Lane Changing

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

scholarly journals End-to-End Automated Lane-Change Maneuvering Considering Driving Style Using a Deep Deterministic Policy Gradient Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5443
Author(s):  
Hongyu Hu ◽  
Ziyang Lu ◽  
Qi Wang ◽  
Chengyuan Zheng
Keyword(s):  
Autonomous Vehicles ◽  
The State ◽  
Steering Wheel ◽  
Lane Change ◽  
Simulation Environment ◽  
Longitudinal Control ◽  
Driving Style ◽  
Lane Changing ◽  
Policy Gradient ◽  
End To End

Changing lanes while driving requires coordinating the lateral and longitudinal controls of a vehicle, considering its running state and the surrounding environment. Although the existing rule-based automated lane-changing method is simple, it is unsuitable for unpredictable scenarios encountered in practice. Therefore, using a deep deterministic policy gradient (DDPG) algorithm, we propose an end-to-end method for automated lane changing based on lidar data. The distance state information of the lane boundary and the surrounding vehicles obtained by the agent in a simulation environment is denoted as the state space for an automated lane-change problem based on reinforcement learning. The steering wheel angle and longitudinal acceleration are used as the action space, and both the state and action spaces are continuous. In terms of the reward function, avoiding collision and setting different expected lane-changing distances that represent different driving styles are considered for security, and the angular velocity of the steering wheel and jerk are considered for comfort. The minimum speed limit for lane changing and the control of the agent for a quick lane change are considered for efficiency. For a one-way two-lane road, a visual simulation environment scene is constructed using Pyglet. By comparing the lane-changing process tracks of two driving styles in a simplified traffic flow scene, we study the influence of driving style on the lane-changing process and lane-changing time. Through the training and adjustment of the combined lateral and longitudinal control of autonomous vehicles with different driving styles in complex traffic scenes, the vehicles could complete a series of driving tasks while considering driving-style differences. The experimental results show that autonomous vehicles can reflect the differences in the driving styles at the time of lane change at the same speed. Under the combined lateral and longitudinal control, the autonomous vehicles exhibit good robustness to different speeds and traffic density in different road sections. Thus, autonomous vehicles trained using the proposed method can learn an automated lane-changing policy while considering safety, comfort, and efficiency.

scholarly journals Goal Estimation of Mandatory Lane Changes Based on Interaction between Drivers

2020 ◽  
Vol 10 (9) ◽  
pp. 3289
Author(s):  
Hanwool Woo ◽  
Mizuki Sugimoto ◽  
Hirokazu Madokoro ◽  
Kazuhito Sato ◽  
Yusuke Tamura ◽  
...  
Keyword(s):  
Warning System ◽  
Autonomous Driving ◽  
Field Method ◽  
Lane Change ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Lane Changing ◽  
Lane Changes ◽  
Dynamic Potential ◽  
Novel Method

In this paper, we propose a novel method to estimate a goal of surround vehicles to perform a lane change at a merging section. Recently, autonomous driving and advance driver-assistance systems are attracting great attention as a solution to substitute human drivers and to decrease accident rates. For example, a warning system to alert a lane change performed by surrounding vehicles to the front space of the host vehicle can be considered. If it is possible to forecast the intention of the interrupting vehicle in advance, the host driver can easily respond to the lane change with sufficient reaction time. This paper assumes a mandatory situation where two lanes are merged. The proposed method assesses the interaction between the lane-changing vehicle and the host vehicle on the mainstream lane. Then, the lane-change goal is estimated based on the interaction under the assumption that the lane-changing driver decides to minimize the collision risk. The proposed method applies the dynamic potential field method, which changes the distribution according to the relative speed and distance between two subject vehicles, to assess the interaction. The performance of goal estimation is evaluated using real traffic data, and it is demonstrated that the estimation can be successfully performed by the proposed method.

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.

Eye Glance Behavior during Lane Changes and Straight-Ahead Driving

2005 ◽  
Vol 1937 (1) ◽  
pp. 44-50 ◽  
Author(s):  
Erik C. B. Olsen ◽  
Suzanne E. Lee ◽  
Walter W. Wierwille
Keyword(s):  
Collision Avoidance ◽  
Blind Spot ◽  
Development Effort ◽  
Lane Change ◽  
Lane Changing ◽  
Lane Changes ◽  
Visual Presence ◽  
Safety And Reliability ◽  
Rearview Mirror ◽  
Straight Ahead

Understanding drivers’ eye behavior before lane changing is an important aspect of designing usable, safe lane-change collision-avoidance systems (LCAS) that will fit well with drivers’ expectations. This understanding could lead to improvements for LCAS as well as for a variety of other collision avoidance systems. Findings regarding driver eye glance behaviors are presented in a comparison of lane change maneuvers with straight-ahead (baseline) driving events. Specific eye glance patterns before lane change initiation were observed. When preparing to make a lane change to the left as compared with driving straight ahead, drivers doubled the number of glances toward the rearview mirror and were much more likely to look at other locations associated with moving to the left, including the left mirror and blind spot. On the basis of the eye glance patterns observed and previous results, the following recommendations are made: ( a) visual presence detection indicator displays should be used to provide information about vehicles in the rear adjacent lane any time a vehicle is detected, ( b) a presence indicator should be presented in a visual format, and ( c) the left mirror and rearview mirror locations should be considered for providing lane change information to the driver. The process of acquiring and analyzing eye glance movements is well worth the investment in resources. However, prototype systems must be tested before implementation, and the exact location and format of warning systems warrant a separate research and development effort to ensure safety and reliability.

Lane Change Rates at Freeway Weaving Sites: Trends in HCM6 and from NGSIM Trajectories

2019 ◽  
Vol 2673 (5) ◽  
pp. 627-636 ◽  
Author(s):  
Ishtiak Ahmed ◽  
Dezhong Xu ◽  
Nagui Rouphail ◽  
Alan Karr
Keyword(s):  
Empirical Data ◽  
Segment Length ◽  
Level Of Service ◽  
Lane Change ◽  
Travel Times ◽  
Trajectory Data ◽  
Freeway Traffic ◽  
Lane Changes

Concerns have been raised about the HCM6 weaving method’s lack of sensitivity to weaving segment length. This study explores the trends in HCM6 as they relate to lane change estimates and their impact on the segment speed and level of service (LOS). The study also compares HCM6 estimates of lane changes against empirical data from an NGSIM weaving site. Thus, the objectives of this study are twofold: ( a) critically investigate the effect of weaving length on lane change and associated speed model estimates in HCM6, and ( b) analyze trends in lane changes against congestion levels using detailed NGSIM trajectory data, comparing against HCM6 estimates. For ( a) it was found that the lack of sensitivity to weave length is because of the absence of this parameter in the nonweaving lane change and speed models. For ( b), a comparison of HCM6 lane change rates with NGSIM, US-101 data confirmed that the HCM6 estimates for weaving vehicles are fully consistent with those at the NGSIM site, controlling for density. In contrast, nonweaving lane change estimates in HCM6 did not deliver the expected trends, with more discretionary lane changes predicted as congestion increased. Finally, analysis of lane change patterns at the NGSIM site revealed a tendency for early merging for freeway to ramp traffic and uniform merging for ramp to freeway traffic over the length of the weave. Interestingly, a speed analysis showed that in most cases, a higher frequency of discretionary lane changes yielded lower travel times for drivers executing them.

Categorizing Merging and Diverging Strategies of Truck Drivers at Motorway Ramps and Weaving Sections using a Trajectory Dataset

2020 ◽  
Vol 2674 (9) ◽  
pp. 855-866
Author(s):  
Salil Sharma ◽  
Maaike Snelder ◽  
Lóránt Tavasszy ◽  
Hans van Lint
Keyword(s):  
Finite Mixture Models ◽  
Research Area ◽  
Truck Drivers ◽  
Lane Change ◽  
Trajectory Data ◽  
Microscopic Simulation ◽  
Data Set ◽  
Lane Changing ◽  
Change Behavior ◽  
Essential Components

Lane-changing models are essential components for microscopic simulation. Although the literature recognizes that different classes of vehicles have different ways of performing lane-change maneuvers, lane change behavior of truck drivers is an overlooked research area. We propose that truck drivers are heterogeneous in their lane change behavior too and that inter-driver differences within truck drivers exist. We explore lane changing behavior of truck drivers using a trajectory data set collected around motorway bottlenecks in the Netherlands which include on-ramp, off-ramp, and weaving sections. Finite mixture models are used to categorize truck drivers with respect to their merging and diverging maneuvers. Indicator variables include spatial, temporal, kinematic, and gap acceptance characteristics of lane-changing maneuvers. The results suggest that truck drivers can be categorized into two and three categories with respect to their merging and diverging behaviors, respectively. The majority of truck drivers show a tendency to merge or diverge at the earliest possible opportunity; this type of behavior leads to most of the lane change activity at the beginning of motorway bottlenecks, thus contributing to the raised level of turbulence. By incorporating heterogeneity within the lane-changing component, the accuracy and realism of existing microscopic simulation packages can be improved for traffic and safety-related assessments.

scholarly journals Prediction of Lane-Changing Maneuvers with Automatic Labeling and Deep Learning

2020 ◽  
Vol 2674 (7) ◽  
pp. 336-347 ◽  
Author(s):  
Vishal Mahajan ◽  
Christos Katrakazas ◽  
Constantinos Antoniou
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Highway Safety ◽  
Unlabeled Data ◽  
Support Vector ◽  
False Alarms ◽  
Lane Changing ◽  
Lane Changes ◽  
Significant Research

Highway safety has attracted significant research interest in recent years, especially as innovative technologies such as connected and autonomous vehicles (CAVs) are fast becoming a reality. Identification and prediction of driving intention are fundamental for avoiding collisions as it can provide useful information to drivers and vehicles in their vicinity. However, the state-of-the-art in maneuver prediction requires the utilization of large labeled datasets, which demand a significant amount of processing and might hinder real-time applications. In this paper, an end-to-end machine learning model for predicting lane-change maneuvers from unlabeled data using a limited number of features is developed and presented. The model is built on a novel comprehensive dataset (i.e., highD) obtained from German highways with camera-equipped drones. Density-based clustering is used to identify lane-changing and lane-keeping maneuvers and a support vector machine (SVM) model is then trained to learn the boundaries of the clustered labels and automatically label the new raw data. The labeled data are then input to a long short-term memory (LSTM) model which is used to predict maneuver class. The classification results show that lane changes can efficiently be predicted in real-time, with an average detection time of at least 3 s with a small percentage of false alarms. The utilization of unlabeled data and vehicle characteristics as features increases the prospects of transferability of the approach and its practical application for highway safety.

Control and Validation of Automated Lane Centering and Lane Changing Maneuver

2009 ◽  
Author(s):  
Jin-Woo Lee ◽  
Bakhtiar Litkouhi
Keyword(s):  
Control Algorithm ◽  
Control Method ◽  
Lateral Displacement ◽  
Lateral Acceleration ◽  
Lane Change ◽  
Time Step ◽  
Lane Changing ◽  
Current Time ◽  
On The Road ◽  
Lateral Offset

This paper describes an automated lane changing control system that has been developed at General Motors Research and Development. This system uses a single monochrome camera to recognize lane markings on the road ahead and uses multiple short range radars to detect surrounding traffic and objects. A sensing unit calculates the host vehicle’s lateral displacement and the heading angle from the center of its lane as well as the relative distance and relative speed of each object. Since the smoothness and comfort of lane change maneuvering are important measures of the control performance, the vehicle dynamic model is integrated into the desired path generation and vehicle’s controller design to reduce the vehicle lateral acceleration and the lateral offset overshoot during the lane change maneuvering. To avoid heavy computation, a simplified model predictive control algorithm is proposed. The control algorithm calculates a steering angle command at the current time step to drive the vehicle to the desired path. The control method and algorithms are implemented on a demonstration vehicle and validated at straight roads and various curve roads of up to 0.001 [1/m] curvature for different vehicle speeds up to 100 [km/hr]. The results show that lane change maneuvering is successfully completed with lateral offset error less than 20 cm.

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