scholarly journals Target Vehicle Selection Algorithm Based on Lane-changing Intention of Preceding Vehicle for ACC

2021 ◽  
Author(s):  
Jun Yao ◽  
Guoying Chen ◽  
Zhenhai Gao
Keyword(s):  
Ride Comfort ◽  
Lateral Position ◽  
Prediction Algorithm ◽  
Lane Change ◽  
Selection Algorithm ◽  
Cruise Control ◽  
Lane Changing ◽  
Target Vehicle ◽  
Preceding Vehicle ◽  
The Subject

Abstract In order to improve the ride comfort and safety of the traditional adaptive cruise control (ACC) system when the preceding vehicle changes lanes, this paper proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention of the preceding vehicle. First, NGSIM dataset is used to train a lane-changing intention prediction algorithm based on sliding window SVM, and the lane-changing intent of the preceding vehicle in the current lane can be identified by lateral position offset. Secondly, according to the lane-changing intention and the collision threat of the preceding vehicle, the target vehicle selection algorithm is studied under three different conditions: safe lane-changing condition, dangerous lane-changing condition, and lane-changing cancellation condition. Finally, the effectiveness of the algorithm proposed in this paper is verified in the co-simulation platform. The simulation results show that the target vehicle selection algorithm proposed in this paper 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 the lane change. In the case of a dangerous lane change, the target vehicle selection algorithm proposed in this paper can respond to the dangerous lane change in advance compared with the target vehicle selection method of the traditional ACC system, which can effectively avoid collisions and improve the safety of the subject vehicle.

scholarly journals Target Vehicle Selection Algorithm Based on Lane-changing Intention of Preceding Vehicle for ACC

2020 ◽  
Author(s):  
Jun Yao ◽  
Guoying Chen ◽  
Zhenhai Gao
Keyword(s):  
Ride Comfort ◽  
Lateral Position ◽  
Prediction Algorithm ◽  
Lane Change ◽  
Selection Algorithm ◽  
Cruise Control ◽  
Lane Changing ◽  
Target Vehicle ◽  
Preceding Vehicle ◽  
The Subject

Abstract In order to improve the ride comfort and safety of the traditional adaptive cruise control (ACC) system when the preceding vehicle changes lanes, this paper proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention of the preceding vehicle. First, NGSIM dataset is used to train a lane-changing intention prediction algorithm based on sliding window SVM, and the lane-changing intent of the preceding vehicle in the current lane can be identified by lateral position offset. Secondly, according to the lane-changing intention and the collision threat of the preceding vehicle, the target vehicle selection algorithm is studied under three different conditions: safe lane-changing condition, dangerous lane-changing condition, and lane-changing cancellation condition. Finally, the effectiveness of the algorithm proposed in this paper is verified in the co-simulation platform. The simulation results show that the target vehicle selection algorithm proposed in this paper 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 the lane change. In the case of a dangerous lane change, the target vehicle selection algorithm proposed in this paper can respond to the dangerous lane change in advance compared with the target vehicle selection method of the traditional ACC system, which can effectively avoid collisions and improve the safety of the subject vehicle.

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

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.

Microscopic Modeling of the Effects of Autonomous Vehicles on Motorway Performance

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.

Capacity Analysis and Cooperative Lane Changing for Connected and Automated Vehicles: Entropy-Based Assessment Method

2019 ◽  
Vol 2673 (8) ◽  
pp. 485-498 ◽  
Author(s):  
Yunpeng Shi ◽  
Qing He ◽  
Zhitong Huang
Keyword(s):  
Assessment Method ◽  
Performance Measure ◽  
Driver Model ◽  
Lane Change ◽  
Cruise Control ◽  
Automated Vehicles ◽  
Lane Changing ◽  
V2v Communication ◽  
Simulation Performance ◽  
Block Entropy

Connected and automated vehicles (CAVs) are poised to transform how we manage and control the existing traffic. CAVs can provide accurate distance sensing and adaptive cruise control which make shorter headway possible, and will eventually increase the roadway throughput or capacity. The vehicle-to-vehicle (V2V) communication technology equipment on CAVs allows vehicles to exchange information and form platoons more efficiently. This paper uses the intelligent driver model (IDM) as the behavior model to simulate CAVs in mixed traffic conditions with both CAVs and human-driven vehicles (HDVs) under different CAV penetration rates. A cooperative CAV lane-changing model is introduced to build more CAV platoons. The model develops two lane-changing algorithms. Partial CAV lane change (PAL) is applied at low CAV percentages, whereas full CAV lane change (FAL) is used at high CAV percentages. In addition, block entropy is employed as a performance measure for lane-changing results. The simulation experiments show that capacity will increase as the CAV percentage grows, and the peak growth rates occur in medium CAV percentage between 40% and 70%. The cooperative CAV lane-changing algorithm is found to decrease HDV–CAV conflicts remarkably by 37% as well as to marginally increase capacity by 2.5% under all CAV percentages. The simulation performance suggests that the threshold of CAV penetration rate for switching PAL to FAL is approximately 55%. Furthermore, it is demonstrated that block entropy can measure CAV lane-changing performance efficiently and represent capacity changes to some extent.

scholarly journals Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4199 ◽  
Author(s):  
Kai Gao ◽  
Di Yan ◽  
Fan Yang ◽  
Jin Xie ◽  
Li Liu ◽  
...  
Keyword(s):  
Potential Field ◽  
Ride Comfort ◽  
Dynamic Performance ◽  
Autonomous Vehicle ◽  
Support Vector ◽  
Artificial Potential Field ◽  
Lane Change ◽  
Lane Changing ◽  
Safety Control ◽  
Car Following

Car-following is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the car-following control of the autonomous vehicle, especially when the vehicle changing lanes is only a connected unintelligent vehicle without expensive and accurate sensors. Autonomous vehicles suffer from adjacent vehicles’ abrupt lane changes, which may reduce ride comfort and increase energy consumption, and even lead to a collision. A machine learning-based lane change intention prediction and real time autonomous vehicle controller is proposed to respond to this problem. First, an interval-based support vector machine is designed to predict the vehicles’ lane change intention utilizing limited low-level vehicle status through vehicle-to-vehicle communication. Then, a conditional artificial potential field method is used to design the car-following controller by incorporating the lane-change intentions of the vehicle. Experimental results reveal that the proposed method can estimate a vehicle’s lane change intention more accurately. The autonomous vehicle avoids collisions with a lane-changing connected unintelligent vehicle with reliable safety and favorable dynamic performance.

Lane-Change Gaming Decision Control Based on Multiple Targets Evaluation for Autonomous Vehicle

2021 ◽  
pp. 036119812110111
Author(s):  
Yangyang Wang ◽  
Hangyun Deng ◽  
Guangda Chen
Keyword(s):  
Ride Comfort ◽  
Autonomous Vehicle ◽  
Objective Evaluation ◽  
Smooth Transition ◽  
Longitudinal Motion ◽  
Lane Change ◽  
Lane Changing ◽  
New Model ◽  
Car Following ◽  
Traffic Efficiency

Automatic lane change is one of the most important highway operations. It seriously affects traffic efficiency and safety. It is also an important driving technology for automatic driving. To achieve the best automatic lane-change control, it is necessary to achieve the control from the perspective of multi-objective evaluation. In this paper, to make it applicable for a hybrid condition of car following and lane change, the traditional car-following model is modified by regarding the longitudinal motion during the lane-changing process as a transition of the car-following behavior in the two lanes before and after a certain lane-change behavior. A hyperbolic tangent transition function is introduced to connect the model to achieve a smooth transition of the model output. Then, the discretionary lane-change decision process of highway autonomous vehicles is modeled into a two-vehicle game model, and a comprehensive loss function concerning safety, efficiency, and ride comfort is proposed for the evaluation of the strategies. The optimal strategy is obtained by minimizing the expectation of losses. Finally, to verify the performance of the proposed new model, simulations of different car-following and lane-changing models are carried out, which is for multi-target simulation conditions. The results of the simulation show that the new model exhibits higher traffic efficiency, better homogeneity, and stability.

Hidden Markov Model of Lane-Changing-Based Car-Following Behavior on Freeways using Naturalistic Driving Data

2021 ◽  
pp. 036119812199938
Author(s):  
Li Zhao ◽  
Laurence Rilett ◽  
Mm Shakiul Haque
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Vehicle Body ◽  
Lane Change ◽  
Lane Changing ◽  
Car Following ◽  
Pilot Model ◽  
Naturalistic Driving

This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3 s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86 s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.

scholarly journals Automatic Lane-Changing Decision Based on Single-Step Dynamic Game with Incomplete Information and Collision-Free Path Planning

Actuators ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 173
Author(s):  
Hongbo Wang ◽  
Shihan Xu ◽  
Longze Deng
Keyword(s):  
Path Planning ◽  
Dynamic Game ◽  
Single Step ◽  
Bézier Curve ◽  
Vehicle Safety ◽  
Bezier Curve ◽  
Lane Change ◽  
Hardware In The Loop ◽  
Power Performance ◽  
Lane Changing

Traffic accidents are often caused by improper lane changes. Although the safety of lane-changing has attracted extensive attention in the vehicle and traffic fields, there are few studies considering the lateral comfort of vehicle users in lane-changing decision-making. Lane-changing decision-making by single-step dynamic game with incomplete information and path planning based on Bézier curve are proposed in this paper to coordinate vehicle lane-changing performance from safety payoff, velocity payoff, and comfort payoff. First, the lane-changing safety distance which is improved by collecting lane-changing data through simulated driving, and lane-changing time obtained by Bézier curve path planning are introduced into the game payoff, so that the selection of the lane-changing start time considers the vehicle safety, power performance and passenger comfort of the lane-changing process. Second, the lane-changing path without collision to the forward vehicle is obtained through the constrained Bézier curve, and the Bézier curve is further constrained to obtain a smoother lane-changing path. The path tracking sliding mode controller of front wheel angle compensation by radical basis function neural network is designed. Finally, the model in the loop simulation and the hardware in the loop experiment are carried out to verify the advantages of the proposed method. The results of three lane-changing conditions designed in the hardware in the loop experiment show that the vehicle safety, power performance, and passenger comfort of the vehicle controlled by the proposed method are better than that of human drivers in discretionary lane change and mandatory lane change scenarios.

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.

Researches on Adaptive Cruise Control system: A state of the art review

2021 ◽  
pp. 095440702110192
Author(s):  
Liangyao Yu ◽  
Ruyue Wang
Keyword(s):  
Adaptive Cruise Control ◽  
Driving Safety ◽  
Cruise Control ◽  
Driver Assistance Systems ◽  
Human Machine Interaction ◽  
System A ◽  
Cruise Control System ◽  
Preceding Vehicle ◽  
Energy Economy ◽  
Machine Interaction

Adaptive Cruise Control (ACC) is one of Advanced Driver Assistance Systems (ADAS) which takes over vehicle longitudinal control under necessary driving scenarios. Vehicle in ACC mode automatically adjusts speed to follow the preceding vehicle based on evaluation of the surrounding traffic. ACC reduces drivers’ workload as well as improves driving safety, energy economy, and traffic flow. This article provides a comprehensive review of the researches on ACC. Firstly, an overview of ACC controller and applied control theories are introduced. Their principles and performances are discussed. Secondly, several application cases of ACC control algorithms are presented. Then validation work including simulation, Hardware-in-the-Loop (HiL) test and on-road experiment is descripted to provide ideas for testing ACC systems for different aims and fidelities. In addition, studies on human-machine interaction are also summarized in this review to provide insights on development of ACC from the perspective of users. At last, challenges and potential directions in this field is discussed, including consideration of vehicle dynamics properties, contradiction between algorithm performance and computation as well as integration of ACC to other intelligent functions on vehicles.

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