Fuzzy logic and enhanced MOBIL model for a type lane-changing decisions in mixed traffic

Modelling heavy vehicle lane changing

Investigation of lane-changing behaviour in a connected environment

<div class="section abstract"><div class="htmlview paragraph">Segment with lane drops are very important in freeway systems since they are major constrains to traffic flow and safety. The frequency of capacity reductions and higher safety risks is proportional to an increase in lane-changing actions, which worsen traffic congestion, decrease road capacity, and increase the risk of an accident. Traditional traffic management strategies that rely on physical structures and driver’s decision making often fail under such conditions. This paper provides a detailed lane change control strategy specific to freeway segments with lane reduction in the connected and autonomous vehicle (CAV) environment. The strategy combines both centralized and decentralized techniques to improve the vehicle’s lane-changing behavior and density. A cellular transmission model of lane-level is proposed for the centralized control of the linked vehicles based on the ratio of the driver compliance. The model derives the density equation and transforms the lane-changing problem before the work zone into merging traffic flow problem. The optimization model is developed based on the total trip time, density deviation, and total lane changes, with constraints on the cell reception capacity and lane changing ratios. Control parameters for lane change distribution are identified using genetic algorithms to solve the problem. For the decentralized control, a reinforcement learning solution is introduced which uses deep Q-networks (DQN) to improve lane-changing actions. The reward function takes into account the traffic efficiency and the impact of lane changing, and the continuous action space is discretized for application. The control mechanism is evaluated by the simulation of a work zone scenario that includes two restricted lanes on the Shanghai-Nanjing Expressway. It also shows that there is an improvement of 3% to 6% in traffic flow and velocity as compared to single-strategy approaches. The collaborative control strategy significantly enhances traffic flow and reduces congestion at bottlenecks and offers valuable information for future traffic control in CAV environments.</div></div>

Current autonomous vehicle (AV) simulators are built to provide large-scale testing required to prove capabilities under varied conditions in controlled, repeatable fashion. However, they have certain failings including the need for user expertise and complex inconvenient tutorials for customized scenario creation. Simulation of Urban Mobility (SUMO) simulator, which has been presented as an open-source traffic simulation platform, has found use as an AV simulator but suffers from similar issues which makes it difficult for entry-level practitioners to utilize the simulator without significant time investment. In that regard, we provide two enhancements to SUMO simulator geared towards massively improving user experience and providing real-life like variability for surrounding traffic. Firstly, we calibrate a car-following model, Intelligent Driver Model (IDM), for highway and urban naturalistic driving data and sample randomly from the parameter distributions to create realistic background vehicle driving behavior. Secondly, we combine SUMO with OpenAI gym, creating a Python package placed in a docker container which can run simulations based on real world highway and urban layouts with generic output observations and input actions that can be processed via any AV pipeline. For the calibration, we provide results using simulated and real-life data. For the Sumo-Gym package, we showcase a simple AV platform which runs IDM and lane change throughout the highway loop and provide some qualitative results. Our aim through these enhancements is to provide an easy-to-use simulation environment which can be installed in any operating platform and can be readily used for AV testing and validation.

Safe autonomous lane changes and impact on traffic flow in a connected vehicle environment

Originally, decision and control of the lane change of the vehicle is on the human driver. It is mainly used to increase the individual's benefit such as decreasing travel time. However, the selfish decision on the lane-changing behavior can sometimes make a negative impact on the overall traffic flow. As autonomous vehicle technology develops, modeling lane changing action as well as lane changing decision making falls within the control category of autonomous vehicles. In this study, we focused on decision making of lane change for autonomous vehicles considering traffic flow, and accordingly, we propose a lane change control system considering whole traffic flow. The lane change control system predicts the future traffic situation using Cell Transmission Model and determines the lane change probability for each lane that minimizes the total time delay through the genetic algorithm. The lane change control system then provides the lane change probability to the vehicles. Performance evaluation of the proposed system in macroscopic simulation shows reduction in the overall travel time delay. The performance of proposed system is also evaluated in microscopic traffic simulation, evaluating the potential performance when it is applied to the actual traffic system: The maximum traffic flow was increased, and the congestion area was greatly reduced and the time required for individual vehicles was reduced.

Connected and automated driving functions are key components for future vehicles. Due to implementation issues and missing infrastructure, the impact of connected and automated vehicles on the traffic flow can only be evaluated in accurate simulations. Simulation of Urban Mobility (SUMO) provides necessary and appropriate models and tools. SUMO contains many car-following models that replicate automated driving, but cannot realistically imitate human driving behavior. When simulating queued vehicles driving off, existing car-following models are neither able to correctly emulate the acceleration behavior of human drivers nor the resulting vehicle gaps. Thus, we propose a time-discrete 2D Human Driver Model to replicate realistic trajectories. We start by combining previously published extensions of the Intelligent Driver Model (IDM) to one generalized model. Discontinuities due to introduced reaction times, estimation errors and lane changes are conquered with new approaches and equations. Above all, the start-up procedure receives more attention than in existing papers. We also provide a first evaluation of the advanced car-following model using 30 minutes of an aerial measurement. This dataset contains three hours of drone recordings from two signalized intersections in Stuttgart, Germany. The method designed for extracting the vehicle trajectories from the raw video data is outlined. Furthermore, we evaluate the accuracy of the trajectories obtained by the aerial measurement using a specially equipped vehicle.

Imitation of Real Lane-Change Decisions Using Reinforcement Learning

Along with the rapid development of autonomous driving technology, autonomous vehicles are showing a trend of practicality and popularity. Autonomous vehicles perceive environmental information through sensors to provide a basis for the decision making of vehicles. Based on this, this paper investigates the lane-changing decision-making behavior of autonomous vehicles. First, the similarity between autonomous vehicles and moving molecules is sought based on a system-similarity analysis. The microscopic lane-changing behavior of vehicles is analyzed by the molecular-dynamics theory. Based on the objective quantification of the lane-changing intention, the interaction potential is further introduced to establish the molecular-dynamics lane-changing model. Second, the relationship between the lane-changing initial time and lane-changing completed time, and the dynamic influencing factors of the lane changing, were systematically analyzed to explore the influence of the microscopic lane-changing behavior on the macroscopic traffic flow. Finally, the SL2015 lane-changing model was compared with the molecular-dynamics lane-changing model using the SUMO platform. SUMO is an open-source and multimodal traffic experimental platform that can realize and evaluate traffic research. The results show that the speed fluctuation of autonomous vehicles under the molecular-dynamics lane-changing model was reduced by 15.45%, and the number of passed vehicles was increased by 5.93%, on average, which means that it has better safety, stability, and efficiency. The molecular-dynamics lane-changing model of autonomous vehicles takes into account the dynamic factors in the traffic scene, and it reasonably shows the characteristics of the lane-changing behavior for autonomous vehicles.

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.

Lane change has a potential significance in road safety. Gap acceptance phenomena serves as a primary and critical phase in lane change maneuver. This study aims to investigate the gap acceptance behaviour of drivers during lane changes on expressways, with a focus on understanding how various factors influence drivers' decisions to change lanes. An extensive dataset collected through various sensors tailored for expressway driving, known as the ‘Expressway Drive: Instrumented Vehicle (EDIV) Dataset’ is utilized. Driving data from 59 drivers covering a distance of around 4000 km was used in the current study. Total 2578 lane changing events are identified through computing lateral deviations measured through 3D LiDAR sensor. Substantial differences are observed within the groups in primary analysis which suggest that lane-change direction significantly affect gap acceptance. To effectively manage both intra- and inter-cluster variances, this study employs two separate three levels mixed-effects linear models. These models account for the interdependence of gap acceptance characteristics within individual drivers and for different directions of lane changes by incorporating random effects. Furthermore, these models examine relationships between lead/ lag gap acceptance and the various influencing factors as fixed effects. It was found that factors such as speed of the subject vehicle, gap position, relative speeds, and surrounding vehicle types had influence on gap acceptance during lane changes on expressways. The insights gained from this study could inform the development of advanced driver assistance systems (ADAS) as well as development of autonomous vehicles, contributing to improved road safety and traffic flow management in high-speed environments.

Lane changing is a crucial scenario in traffic environments, and accurately recognizing and predicting lane-changing behavior is essential for ensuring the safety of both autonomous vehicles and drivers. Through considering the multi-vehicle information interaction characteristics in lane-changing behavior for vehicles and the impact of driver experience needs on lane-changing decisions, this paper proposes a lane-changing model for vehicles to achieve safe and comfortable driving. Firstly, a lane-changing intention recognition model incorporating interaction effects was established to obtain the initial lane-changing intention probability of the vehicles. Secondly, by accounting for individual driving styles, a lane-changing behavior decision model was constructed based on a Gaussian mixture hidden Markov model (GMM-HMM) along with a parameter estimation method. The initial lane-changing intention probability serves as the input for the decision model, and the final lane-changing decision is made by comparing the probabilities of lane-changing and non-lane-changing scenarios. Finally, the model was validated using real-world data from the Next Generation Simulation (NGSIM) dataset, with empirical results demonstrating its high accuracy in recognizing and predicting lane-changing behavior. This study provides a robust framework for enhancing lane-changing decision making in complex traffic environments.

A dynamic lane-changing decision and trajectory planning model of autonomous vehicles under mixed autonomous vehicle and human-driven vehicle environment

A cooperative lane change model for connected and autonomous vehicles on two lanes highway by considering the traffic efficiency on both lanes

This paper aims to investigate the characteristics of discretionary lane change (LC) duration on freeways based on an enriched dataset that contains the LC vehicle trajectories of 2905 passenger cars and 433 heavy vehicles. The LC duration is comprehensively analysed, and four stochastic LC duration models are established according to vehicle type and LC direction. LC duration varies with vehicle type and LC direction. Considering driver heterogeneity, accelerated failure time (AFT) models with fixed parameters, latent classes, and random parameters were established in this paper. The results show that drivers of heavy vehicles display greater heterogeneity and that vehicle types and LC directions have significant influence on the LC duration. The results of this study are helpful for understanding the mechanism of the LC process and the influence of LC on traffic flow and improving the safety of lane-changing behaviours of connected and autonomous vehicles.