A Conflict Duration Graph-Based Coordination Method for Connected and Automated Vehicles at Signal-Free Intersections

Previous studies on Connected and Automated Vehicles (CAVs) demonstrated the potential to coordinate the behaviors of multiple connected vehicles for traffic improvements. In this paper, we first propose a Conflict Duration Graph-based (CDG-based) coordination framework to resolve collisions and improve the traffic capacity of signal-free intersections. Secondly, a Speed Control-based Intersection Coordination Model (SICM) is developed to identify complex constraints in multi-vehicle collision scenarios. Thirdly, a geometric Translation-based Intersection Coordination Algorithm (TICA) is proposed to calculate the ideal location of time blocks in CDGs and then obtain the near-optimal design speed in the form of combinatorial optimization. Twelve groups of test scenarios with different traffic volumes were designed and tested on a MATLAB-based simulation platform. Simulation results showed that the proposed method can resolve all the collisions and instruct the vehicles to pass signal-free intersections collaboratively without stopping in low to medium level of congestion.

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Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211070286 ◽

2022 ◽

pp. 036119812110702

Author(s):

Shunchao Wang ◽

Zhibin Li ◽

Bingtong Wang ◽

Jingfeng Ma ◽

Jingcai Yu

Keyword(s):

Motion Planning ◽

Collision Avoidance ◽

Motion Tracking ◽

Collision Risk ◽

Automated Vehicles ◽

Vehicle Collision ◽

Planning Framework ◽

Planning Algorithm ◽

Velocity Obstacle ◽

Vehicle Dynamic Model

This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.

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Vehicle Tracking and Counting System in Dusty Weather with Vibrating Camera Conditions

Journal of Sensors ◽

10.1155/2017/3812301 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Nastaran Yaghoobi Ershadi ◽

José Manuel Menéndez

Keyword(s):

Traffic Control ◽

Gaussian Model ◽

Weather Conditions ◽

Surveillance Systems ◽

Traffic Surveillance ◽

Traffic Conditions ◽

Detection And Tracking ◽

Traffic Volumes ◽

Medium Level ◽

Bad Weather

Traffic surveillance systems are interesting to many researchers to improve the traffic control and reduce the risk caused by accidents. In this area, many published works are only concerned about vehicle detection in normal conditions. The camera may vibrate due to wind or bridge movement. Detection and tracking of vehicles are a very difficult task when we have bad weather conditions in winter (snowy, rainy, windy, etc.) or dusty weather in arid and semiarid regions or at night, among others. In this paper, we proposed a method to track and count vehicles in dusty weather with a vibrating camera. For this purpose, we used a background subtraction based strategy mixed with extra processing to segment vehicles. In this paper, the extra processing included the analysis of the headlight size, location, and area. In our work, tracking was done between consecutive frames via a particle filter to detect the vehicle and pair the headlights using the connected component analysis. So, vehicle counting was performed based on the pairing result. Our proposed method was tested on several video surveillance records in different conditions such as in dusty or foggy weather, with a vibrating camera, and on roads with medium-level traffic volumes. The results showed that the proposed method performed better than other previously published methods, including the Kalman filter or Gaussian model, in different traffic conditions.

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Area-wide real-world test scenarios of poor visibility for safe development of automated vehicles

European Transport Research Review ◽

10.1186/s12544-018-0304-x ◽

2018 ◽

Vol 10 (2) ◽

Cited By ~ 1

Author(s):

Thomas Winkle ◽

Christian Erbsmehl ◽

Klaus Bengler

Keyword(s):

Real World ◽

Automated Vehicles ◽

Test Scenarios

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Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms

2019 IEEE Intelligent Vehicles Symposium (IV) ◽

10.1109/ivs.2019.8814230 ◽

2019 ◽

Cited By ~ 8

Author(s):

Moritz Klischat ◽

Matthias Althoff

Keyword(s):

Evolutionary Algorithms ◽

Automated Vehicles ◽

Critical Test ◽

Test Scenarios

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Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Journal of Advanced Transportation ◽

10.1155/2018/2493401 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Ahmed Hussein ◽

Pablo Marín-Plaza ◽

Fernando García ◽

José María Armingol

Keyword(s):

Task Allocation ◽

Feasible Solution ◽

Intelligent Vehicles ◽

Hybrid Optimization ◽

Automated Vehicles ◽

Cooperative Driving ◽

And Performance ◽

Self Driving Cars ◽

Test Scenarios ◽

Significant Attention

Self-driving cars are attracting significant attention during the last few years, which makes the technology advances jump fast and reach a point of having a number of automated vehicles on the roads. Therefore, the necessity of cooperative driving for these automated vehicles is exponentially increasing. One of the main issues in the cooperative driving world is the Multirobot Task Allocation (MRTA) problem. This paper addresses the MRTA problem, specifically for the problem of vehicles and requests allocation. The objective is to introduce a hybrid optimization-based approach to solve the problem of multiple intelligent vehicles requests allocation as an instance of MRTA problem, to find not only a feasible solution, but also an optimized one as per the objective function. Several test scenarios were implemented in order to evaluate the efficiency of the proposed approach. These scenarios are based on well-known benchmarks; thus a comparative study is conducted between the obtained results and the suboptimal results. The analysis of the experimental results shows that the proposed approach was successful in handling various scenarios, especially with the increasing number of vehicles and requests, which displays the proposed approach efficiency and performance.

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Study on the Test Scenarios of Level 2 Automated Vehicles

2018 IEEE Intelligent Vehicles Symposium (IV) ◽

10.1109/ivs.2018.8500600 ◽

2018 ◽

Cited By ~ 1

Author(s):

Li Huang ◽

Qin Xia ◽

Fei Xie ◽

Hai-Lin Xiu ◽

Hong Shu

Keyword(s):

Automated Vehicles ◽

Test Scenarios ◽

Level 2

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An Optimal Coordination Framework for Connected and Automated Vehicles in two Interconnected Intersections

2019 IEEE Conference on Control Technology and Applications (CCTA) ◽

10.1109/ccta.2019.8920448 ◽

2019 ◽

Cited By ~ 1

Author(s):

Behdad Chalaki ◽

Andreas A. Malikopoulos

Keyword(s):

Automated Vehicles ◽

Optimal Coordination ◽

Coordination Framework

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Cross-Sectional Study of Vehicle Speeds on Rural Four-Lane Highway Curves

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120939096 ◽

2020 ◽

Vol 2674 (10) ◽

pp. 15-27

Author(s):

Michael P. Pratt ◽

Srinivas R. Geedipally ◽

Minh Le

Keyword(s):

Cross Sectional Study ◽

Cross Sectional ◽

Horizontal Curves ◽

Crash Rates ◽

Operational Characteristics ◽

Document Models ◽

Traffic Volumes ◽

Central Texas ◽

Design Speed ◽

Curve Radius

Research has consistently shown that horizontal curves are often associated with increased crash rates compared with similar tangent highway sections. These crashes are often related to speed and the difficulty of judging the severity of the curve. Curve speed models are used for a variety of applications, including assessing operational characteristics, evaluating design speed, conducting spot safety analyses, and setting curve advisory speeds. However, most of the documented curve speed models apply to rural two-lane highways, while relatively few models have been developed for rural multilane highways. These types of highways may exhibit different driver behavior in curves because of their more generous geometric design and higher traffic volumes. The objective of this paper is to document models that have been developed for several types of rural four-lane highways, including undivided highways, divided highways, and freeways. The authors developed models that account for geometric characteristics like curve radius, superelevation rate, and deflection angle, as well as operational characteristics like approach tangent (TN) speed. These models were calibrated using a database of about 46,000 vehicles across 29 horizontal curve sites in central Texas.

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