Development of Operation Guidelines for Leader–Follower Autonomous Maintenance Vehicles at Work Zone Locations

2021 ◽  
pp. 036119812110566
Author(s):  
Qing Tang ◽  
Xianbiao Hu ◽  
Ruwen Qin
Keyword(s):  
Decision Making ◽  
Autonomous Vehicle ◽  
Field Testing ◽  
Work Zone ◽  
Lane Changing ◽  
The Public ◽  
Car Following ◽  
Technical Requirements ◽  
Car Following Model ◽  
Niche Area

The rapid advancement of connected and autonomous vehicle (CAV) technologies, although possibly years away from wide application to the general public travel, are receiving attention from many state Departments of Transportation (DOT) in the niche area of using autonomous maintenance technology (AMT) to reduce fatalities of DOT workers in work zone locations. Although promising results are shown in testing and deployments in several states, current autonomous truck mounted attenuator (ATMA) system operators are not provided with much practical driving guidance on how to drive these new vehicle systems in a way that is safe to both the public and themselves. To this end, this manuscript aims to model and develop a set of rules and instructions for ATMA system operators, particularly when it comes to critical locations where essential decision making is needed. Specifically, three technical requirements are investigated: car-following distance, critical lane-changing gap distance, and intersection clearance time. Newell’s simplified car-following model, and the classic lane-changing behavior model are modified, with roll-ahead distance taken into account, to model the driving behaviors of the ATMA vehicles at those critical decision-making locations. Data are collected from real-world field testing to calibrate and validate the developed models. The modeling outputs suggest important thresholds for ATMA system operators to follow. For example, on a freeway with a speed limit of 70 mph and ATMA operating speed of 10 mph, car-following distance should be no less than 75 ft for the lead truck and 100 ft for the follower truck, the critical lane-changing gap distance is 912 ft, and a minimum intersection clearance is 15 s, which are all much higher than the requirements for a general vehicle.

scholarly journals Trajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Tong Zhu ◽  
Xiaohu Li ◽  
Wei Fan ◽  
Changshuai Wang ◽  
Haoxue Liu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Trajectory Optimization ◽  
Autonomous Vehicle ◽  
Work Zone ◽  
Operating Efficiency ◽  
Lane Changing ◽  
Car Following ◽  
Advisory Service ◽  
Online Computation ◽  
Multiagent Reinforcement Learning

Work zone areas are frequent congested sections considered as the freeway bottleneck. Connected and autonomous vehicle (CAV) trajectory optimization can improve the operating efficiency in bottleneck areas by harmonizing vehicles’ manipulations. This study presents a joint trajectory optimization of cooperative lane changing, merging, and car-following actions for CAV control at a local merging point together with upstream points. The multiagent reinforcement learning (MARL) method is applied in this system, with one agent providing a merging advisory service at the merging point and controlling the inner-lane vehicles’ headway for smooth outer-lane vehicle merging, while other agents provide lane-changing advisory services at advance lane-changing points to control how vehicles make lane changes in advance and perform corresponding headway adjustment, similar to and jointly with the merging advisory service. Uniting all agents, the coordination graph (CG) method is applied to seek the global optimum, overcoming the exponential growth problem in MARL. Using MATLAB and the VISSIM COM interface, an online simulation platform is established. The simulation results show that MARL is effective for online computation with in-timing response. More importantly, comparisons of the results obtained in various scenarios demonstrate that the proposed system obtained smoother vehicle trajectories in all controlled sections, rather than only in the merging area, indicating that it can achieve better traffic conditions in freeway work zone areas.

Simulation and evaluation of speed and lane-changing advisory of CAVS at work zones in heterogeneous traffic flow

2020 ◽  
Vol 34 (21) ◽  
pp. 2050201
Author(s):  
Wenjing Wu ◽  
Renchao Sun ◽  
Anning Ni ◽  
Zhikang Liang ◽  
Hongfei Jia
Keyword(s):  
Traffic Flow ◽  
Control Strategy ◽  
Control Strategies ◽  
Heavy Traffic ◽  
Autonomous Vehicle ◽  
Heterogeneous Traffic ◽  
Work Zone ◽  
Lane Changing ◽  
Advantages And Disadvantages ◽  
Car Following Model

Emerging connected autonomous vehicle (CAV) technologies provide an opportunity to the vehicle motion control to improve the traffic performance. This study simulated and evaluated the CAV-based speed and lane-changing (LC) control strategies at the expressway work zone in heterogeneous traffic flow. The control strategies of CAV are optimized by the multi-layer control structure based on model predictive control. The heterogeneous traffic flow composed of human-driven vehicles and CAVs is constructed based on cellular automata by the proposed Expected Distance-based Symmetric Two-lane Cellular Automate (ED-STCA) LC model and CAV car-following model. The six control strategies composed of variable speed limits (VSL), LC and their coordinated control strategies are experimented. The average travel time and throughput are selected to assess the advantages and disadvantages of each strategy under each combination of vehicles’ arrival rates and CAV mixed ratios. The numerical results show that: (i) the effect of the control strategy on the traffic is not obvious under free flow, and the control strategy may worsen the traffic under medium traffic. (ii) Early lane-changing control (ELC) is better than late lane-changing control (LLC) under medium traffic, and LLC is better under heavy traffic. (iii) [Formula: see text] is the best choice under heavy traffic and the mixed rate of CAVs is high. The simulation results obtained in the paper would provide some practical references for transportation agencies to manage the traffic in work zone under networking environment in the future.

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 Line-of-Sight Obstruction Analysis for Vehicle-to-Vehicle Network Simulations in a Two-Lane Highway Scenario

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Taimoor Abbas ◽  
Fredrik Tufvesson
Keyword(s):  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Path Loss ◽  
Mobility Model ◽  
Line Of Sight ◽  
Shadow Fading ◽  
Lane Changing ◽  
Car Following ◽  
Car Following Model ◽  
The Impact

In vehicular ad-hoc networks (VANETs) the impact of vehicles as obstacles has largely been neglected in the past. Recent studies have reported that the vehicles that obstruct the line-of-sight (LOS) path may introduce 10–20 dB additional loss, and as a result reduce the communication range. Most of the traffic mobility models (TMMs) today do not treat other vehicles as obstacles and thus cannot model the impact of LOS obstruction in VANET simulations. In this paper the LOS obstruction caused by other vehicles is studied in a highway scenario. First a car-following model is used to characterize the motion of the vehicles driving in the same direction on a two-lane highway. Vehicles are allowed to change lanes when necessary. The position of each vehicle is updated by using the car-following rules together with the lane-changing rules for the forward motion. Based on the simulated traffic a simple TMM is proposed for VANET simulations, which is capable to identify the vehicles that are in the shadow region of other vehicles. The presented traffic mobility model together with the shadow fading path-loss model can take into account the impact of LOS obstruction on the total received power in the multiple-lane highway scenarios.

Lane-changing behavior and its effect on energy dissipation using full velocity difference model

2015 ◽  
Vol 27 (02) ◽  
pp. 1650013 ◽  
Author(s):  
Jian Wang ◽  
Jian-Xun Ding ◽  
Qin Shi ◽  
Reinhart D. Kühne
Keyword(s):  
Energy Dissipation ◽  
Urban Traffic ◽  
Security Requirements ◽  
Difference Model ◽  
Fundamental Diagram ◽  
Velocity Difference ◽  
Lane Changing ◽  
Car Following ◽  
Car Following Model ◽  
Specific Speed

In real urban traffic, roadways are usually multilane with lane-specific velocity limits. Most previous researches are derived from single-lane car-following theory which in the past years has been extensively investigated and applied. In this paper, we extend the continuous single-lane car-following model (full velocity difference model) to simulate the three-lane-changing behavior on an urban roadway which consists of three lanes. To meet incentive and security requirements, a comprehensive lane-changing rule set is constructed, taking safety distance and velocity difference into consideration and setting lane-specific speed restriction for each lane. We also investigate the effect of lane-changing behavior on distribution of cars, velocity, headway, fundamental diagram of traffic and energy dissipation. Simulation results have demonstrated asymmetric lane-changing “attraction” on changeable lane-specific speed-limited roadway, which leads to dramatically increasing energy dissipation.

Research on decision-making of autonomous vehicle following based on reinforcement learning method

2019 ◽  
Vol 46 (3) ◽  
pp. 444-452 ◽  
Author(s):  
Hongbo Gao ◽  
Guanya Shi ◽  
Kelong Wang ◽  
Guotao Xie ◽  
Yuchao Liu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning System ◽  
Explicit Calculation ◽  
Gradient Descent Method ◽  
Learning Method ◽  
Content Type ◽  
Car Following

Purpose Over the past decades, there has been significant research effort dedicated to the development of autonomous vehicles. The decision-making system, which is responsible for driving safety, is one of the most important technologies for autonomous vehicles. The purpose of this study is the use of an intensive learning method combined with car-following data by a driving simulator to obtain an explanatory learning following algorithm and establish an anthropomorphic car-following model. Design/methodology/approach This paper proposed car-following method based on reinforcement learning for autonomous vehicles decision-making. An approximator is used to approximate the value function by determining state space, action space and state transition relationship. A gradient descent method is used to solve the parameter. Findings The effect of car-following on certain driving styles is initially achieved through the simulation of step conditions. The effect of car-following initially proves that the reinforcement learning system is more adaptive to car following and that it has certain explanatory and stability based on the explicit calculation of R. Originality/value The simulation results show that the car-following method based on reinforcement learning for autonomous vehicle decision-making realizes reliable car-following decision-making and has the advantages of simple sample, small amount of data, simple algorithm and good robustness.

Multi-Player Dynamic Game-Based Automatic Lane-Changing Decision Model under Mixed Autonomous Vehicle and Human-Driven Vehicle Environment

2020 ◽  
Vol 2674 (11) ◽  
pp. 165-183
Author(s):  
Yuewen Yu ◽  
Shikun Liu ◽  
Peter J. Jin ◽  
Xia Luo ◽  
Mengxue Wang
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Splitting Method ◽  
Dynamic Game ◽  
Cruise Control ◽  
Lane Changing ◽  
Bicycle Model ◽  
Decision Making Model

The lane-changing decision-making process is challenging but critical to ensure safe and smooth maneuvers for autonomous vehicles (AVs). Conventional Gipps-type algorithms lack the flexibility for practical use under a mixed autonomous vehicle and human-driven vehicle (AV-HV) environment. Algorithms based on utility ignore the reactions of surrounding vehicles to the lane-changing vehicle. Game theory is a good way to solve the shortcomings of current algorithms, but most models based on game theory simplify the game with surrounding vehicles to the game with the following vehicle in the target lane, which means that the lane-changing decision under a mixed environment is not realized. This paper proposes a lane-changing decision-making model which is suitable for an AV to change lanes under a mixed environment based on a multi-player dynamic game theory. The overtaking expectation parameter (OEP) is introduced to estimate the utility of the following vehicle, OEP can be calculated by the proposed non-lane-based full velocity difference model with the consideration of lateral move and aggressiveness. This paper further proposes a hybrid splitting method algorithm to obtain the Nash equilibrium solution in the multi-player game to obtain the optimal strategy of lane-changing decision for AVs. An adaptive cruise control simulation environment is developed with MATLAB’s Simulink toolbox using Next Generation Simulation (NGSIM) data as the background traffic flow. The classic bicycle model is used in the control of involved HVs. Simulation results show the efficiency of the proposed multi-player dynamic game-based algorithm for lane-changing decision making by AVs under a mixed AV-HV environment.

Sign in / Sign up

Export Citation Format

Share Document