A dynamic cooperative lane-changing model for connected and autonomous vehicles with possible accelerations of a preceding vehicle

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.

Download Full-text

Decentralized Cooperative Lane-Changing Decision-Making for Connected Autonomous Vehicles*

IEEE Access ◽

10.1109/access.2017.2649567 ◽

2016 ◽

Vol 4 ◽

pp. 9413-9420 ◽

Cited By ~ 35

Author(s):

Jianqiang Nie ◽

Jian Zhang ◽

Wanting Ding ◽

Xia Wan ◽

Xiaoxuan Chen ◽

...

Keyword(s):

Decision Making ◽

Autonomous Vehicles ◽

Lane Changing

Download Full-text

Investigation of lane-changing decision-making models for autonomous vehicles

10.1117/12.2613917 ◽

2021 ◽

Author(s):

Wei Wu ◽

Jiajun Ye ◽

Weiping Tong ◽

Youkai Cui

Keyword(s):

Decision Making ◽

Autonomous Vehicles ◽

Lane Changing ◽

Decision Making Models

Download Full-text

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

Sensors ◽

10.3390/s20185443 ◽

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.

Download Full-text

Characterizing Lane Changes via Digitized Infrastructure and Low-Cost GPS

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119841277 ◽

2019 ◽

Vol 2673 (8) ◽

pp. 298-309 ◽

Cited By ~ 2

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.

Download Full-text

Microscopic Modeling of the Effects of Autonomous Vehicles on Motorway Performance

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120949243 ◽

2020 ◽

Vol 2674 (11) ◽

pp. 697-707

Author(s):

George Mesionis ◽

Mark Brackstone ◽

Natalie Gravett

Keyword(s):

Automotive Industry ◽

Autonomous Vehicles ◽

Behavioral Model ◽

Cruise Control ◽

Lane Changing ◽

Positive Effects ◽

Traffic Conditions ◽

High Penetration ◽

The Subject ◽

Microscopic Modeling

Autonomous vehicles (AVs) have been the subject of extensive research in recent years and are expected to completely transform the operation of transport networks and revolutionize the automotive industry in the coming decades. Modeling detailed interactions among vehicles with varying levels of penetration rates is essential for evaluating the potential effects. One such investigation is being performed within the ‘HumanDrive’ Project in the U.K. This work has required the development of a behavioral model that incorporates microscopic level interactions and has been based on a pre-existing adaptive cruise control and lane-changing model that has been adapted to better replicate the limitations of AVs and allow the investigation of differing levels of intelligence or assertiveness. The model has been implemented on the M1 Motorway near Sheffield in the U.K. This has allowed the investigation of the effects of AVs on the operation of a real network under various traffic conditions where the overall effects may be revealed, both as advantages to AV drivers, and potentially disadvantages to non-AV traffic. Additionally, it has been possible to examine how these affect junction operations and net emissions. Preliminary results have allowed us to quantify the positive effects of AVs which increase with the penetration. However, it is clear that there are points of inflection where benefits start to slow. It is at these (high) penetration rates that initial operational assumptions may become increasingly stretched and additional infrastructure and cooperative systems are likely to have to become prevalent.

Download Full-text

Target Vehicle Selection Algorithm for Adaptive Cruise Control Based on Lane-changing Intention of Preceding Vehicle

Chinese Journal of Mechanical Engineering ◽

10.1186/s10033-021-00650-8 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Jun Yao ◽

Guoying Chen ◽

Zhenhai Gao

Keyword(s):

Adaptive Cruise Control ◽

Prediction Algorithm ◽

Support Vector ◽

Lane Change ◽

Selection Algorithm ◽

Cruise Control ◽

Lane Changing ◽

Target Vehicle ◽

Preceding Vehicle ◽

The Subject

AbstractTo improve the ride comfort and safety of a traditional adaptive cruise control (ACC) system when the preceding vehicle changes lanes, it proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention for the preceding vehicle. First, the Next Generation Simulation dataset is used to train a lane-changing intention prediction algorithm based on a sliding window support vector machine, and the lane-changing intention of the preceding vehicle in the current lane is identified by lateral position offset. Second, according to the lane-changing intention and collision threat of the preceding vehicle, the target vehicle selection algorithm is studied under three different conditions: safe lane-changing, dangerous lane-changing, and lane-changing cancellation. Finally, the effectiveness of the proposed algorithm is verified in a co–simulation platform. The simulation results show that the target vehicle selection algorithm can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels their lane change maneuver. In the case of a dangerous lane change, the target vehicle selection algorithm proposed in this paper can respond more rapidly to a dangerous lane change than the target vehicle selection method of the traditional ACC system; thus, it can effectively avoid collisions and improve the safety of the subject vehicle.

Download Full-text