Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Negash, Natnael M.; Yang, James

doi:10.1109/access.2023.3249144

Cited by 19 publications

(6 citation statements)

References 264 publications

(288 reference statements)

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“…Paper [42] reviewed control techniques for autonomous and connected vehicles. A review of driver behaviour models was proposed in [43].…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Gallo

2024

Smart Cities

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In the near future, fully autonomous vehicles may revolutionise mobility and contribute to the development of the smart city concept. In this work, we assume that vehicles are not only fully autonomous but also centrally controlled by a single operator, who can also define the traffic light control parameters at intersections. With the aim of optimising the system to achieve a global optimum, the operator can define both the routes of the fleet of vehicles and the traffic light control parameters. This paper proposes a model for the joint optimisation of traffic light control parameters and autonomous vehicle routes to achieve the system optimum. The model, which is solved using a gradient algorithm, is tested on networks of different sizes. The results obtained show the validity of the proposed approach and the advantages of centralised management of vehicles and intersection control parameters.

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“…Paper [42] reviewed control techniques for autonomous and connected vehicles. A review of driver behaviour models was proposed in [43].…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Gallo

2024

Smart Cities

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“…[ 14 ] A comprehensive overview of driver behavioural models (DBM) in vehicle‐vehicle interactions can be found in Ref. [15]. Here, the focus will be on DBMs that are relevant to vehicle‐pedestrian interactions.…”

Section: Human Behaviour Studies On Interactionsmentioning

confidence: 99%

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Crosato,

Tian,

Shum

et al. 2023

Advanced Intelligent Systems

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Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the AV to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modeling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modeling, encompassing cognitive methods, machine‐learning approaches, and game‐theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration.

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“…Existing studies often decompose continuous vehicle driving actions into two types of sub-behaviors, following and lane changing, and construct relatively independent modeling systems for them [4][5][6][7][8][9][10][11]. The behavioral assumption of this modeling approach is that since the time a driver spends performing a lane change during continuous driving is shorter than the time they spend in a following state, it is assumed that the following behavior is driven by the decision to follow continuously within a certain spatial and temporal range, whereas the lane-changing behavior is derived from the driver's behavioral decision and the time spent on this decision is very short or even negligible.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Driving Behavior Analysis and Unified Modeling in Urban Road Scenarios

Zhang,

Qu,

Zhang

et al. 2024

Sustainability

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To improve the simulation accuracy and efficiency of microscopic urban traffic, a unified modeling method considering the behavioral characteristics of vehicle drivers is proposed by considering the lane-changing vehicles on the inlet lanes of signalized intersections and their approach following vehicles on the target lanes as research objects. Based on the driver’s multidirectional, multi-vehicle anticipation ability and introducing lateral vehicle influence coefficients, the full velocity difference car-following model was extended to microscopic traffic models that consider the driver’s capacity for multi-directional, multi-vehicle anticipation. The extended model can describe longitudinal movements of lane changing and car followers using lateral vehicle influential parameters. The influences of traffic control signals and the type of lane change on drivers’ decisions were integrated into the model by reformulating the optimal velocity function of the basic car following the model. Similar modeling methods and components were applied to formulate four groups of experimental models and one group of test models. Vehicle trajectory data and manual observations were collected on urban arteries to calibrate and evaluate the research models, experimental models, and test models. The results show that the car-following behavior is more sensitive to the variation in the status of the lateral moving vehicle and change of lane-changing type compared to lane-changing behavior during the lane-changing process. In addition, when lane changing gradually encroaches on the target lane, the vehicle observes the driving conditions and adjusts its driving behaviors differently. This research helps to analyze travel characteristics and influence mechanisms of vehicles on urban roads, which is a guide for the future development of sustainable transportation and self-driving vehicles and promoting the efficient operation of urban transportation systems.

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Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Cited by 19 publications

References 264 publications

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Vehicle Driving Behavior Analysis and Unified Modeling in Urban Road Scenarios

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