Vehicle control algorithms for cooperative driving with automated vehicles and intervehicle communications

2002 ◽  
Vol 3 (3) ◽  
pp. 155-161 ◽  
Author(s):  
S. Kato ◽  
S. Tsugawa ◽  
K. Tokuda ◽  
T. Matsui ◽  
H. Fujii
Keyword(s):  
Vehicle Control ◽  
Control Algorithms ◽  
Automated Vehicles ◽  
Cooperative Driving

scholarly journals Effect of Automation Instructions and Vehicle Control Algorithms on Eye Behavior in Highly Automated Vehicles

2019 ◽  
Vol 10 (1) ◽  
pp. 73-79 ◽  
Author(s):  
Morgan Price ◽  
John Lee ◽  
Azadeh Dinparastdjadid ◽  
Heishiro Toyoda ◽  
Joshua Domeyer
Keyword(s):  
Vehicle Control ◽  
Control Algorithms ◽  
Automated Vehicles

scholarly journals Experimental Study on Different Types of Curves for Ride Comfort in Automated Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Naohisa Hashimoto ◽  
Yusuke Takinami ◽  
Makoto Yamamoto
Keyword(s):  
Traffic Accidents ◽  
Field Experiments ◽  
Ride Comfort ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Significance Level ◽  
Automated Vehicle ◽  
Driving Assistance ◽  
Human Factor Research ◽  
Automated Vehicle Control

Vehicle automation is among the best possible solutions for traffic issues, including traffic accidents, traffic jams, and energy consumption. However, the user acceptance of automated vehicles is critical and is affected by riding comfort. In addition, human factors in automated vehicle control should be clear. This study evaluates the effect of different courses on driving comfort in automated vehicles using field experiments with 25 subjects. This study focused on lateral motion, but speed control was not targeted. Further, generating a path for obstacle avoidance and lane keeping, which have several constraining conditions, was also not targeted. Rendering a comfortable path is beneficial for developing an acceptable system as a car developer and for building new curves for automated or driving assistance systems from the perspective of construction. The automated vehicle drove at a speed of 30 km/h on four courses, namely, clothoid, two types of spline curves, and arc, based on the real intersection. Each participant sat on both the driver and passenger seat and answered a questionnaire. The experimental data indicated the clothoid course to be the most comfortable, while the arc was most uncomfortable for a significance level of 1%. These tendencies are applicable to driver and passenger seats, all genders, and experiences and will be beneficial for human factor research in automated vehicle control.

scholarly journals A Novel On-Ramp Merging Strategy for Connected and Automated Vehicles Based on Game Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Haigen Min ◽  
Yukun Fang ◽  
Runmin Wang ◽  
Xiaochi Li ◽  
Zhigang Xu ◽  
...  
Keyword(s):  
Game Theory ◽  
Travel Time ◽  
Fuel Consumption ◽  
Transportation Network ◽  
Control Algorithms ◽  
Traffic Density ◽  
Automated Vehicles ◽  
Network Efficiency ◽  
Total Travel Time ◽  
The Cost

Connected and automated vehicles (CAVs) have attracted much attention of researchers because of its potential to improve both transportation network efficiency and safety through control algorithms and reduce fuel consumption. However, vehicle merging at intersection is one of the main factors that lead to congestion and extra fuel consumption. In this paper, we focused on the scenario of on-ramp merging of CAVs, proposed a centralized approach based on game theory to control the process of on-ramp merging for all agents without any collisions, and optimized the overall fuel consumption and total travel time. For the framework of the game, benefit, loss, and rules are three basic components, and in our model, benefit is the priority of passing the merging point, represented via the merging sequence (MS), loss is the cost of fuel consumption and the total travel time, and the game rules are designed in accordance with traffic density, fairness, and wholeness. Each rule has a different degree of importance, and to get the optimal weight of each rule, we formulate the problem as a double-objective optimization problem and obtain the results by searching the feasible Pareto solutions. As to the assignment of merging sequence, we evaluate each competitor from three aspects by giving scores and multiplying the corresponding weight and the agent with the higher score gets comparatively smaller MS, i.e., the priority of passing the intersection. The simulations and comparisons are conducted to demonstrate the effectiveness of the proposed method. Moreover, the proposed method improved the fuel economy and saved the travel time.

EU CyberCars-2 Final Demo Results: IAI-CSIC Standpoint

2010 ◽  
Vol 22 (6) ◽  
pp. 702-707
Author(s):  
Javier Alonso ◽  
◽  
Joshué Pérez ◽  
Vicente Milanés ◽  
Carlos González ◽  
...  
Keyword(s):  
Software Architecture ◽  
Control Algorithms ◽  
Dual Mode ◽  
Industrial Automation ◽  
Decision Algorithm ◽  
Cooperative Driving ◽  
Cooperative Maneuvers ◽  
Eu Project ◽  
And Control ◽  
Common Architecture

In our work on decision and control algorithms for cooperative driving maneuvers developed by the AUTOPIA group of the IAI-CSIC1 in EU project CyberCars-2 (CC2), we focused on defining and developing the software architecture and procedures enabling transparent cooperation between cybercars and dual-mode cars in complex maneuvers tested in the final CyberCars-2 demo. After briefly outlining a common architecture definition, a detailed study of cooperative maneuvers and an analysis of decision and control primitives for cooperative driving, we report on the final demonstration, the data it provided and its control algorithms. The main contributors to this work are: IAI-CSIC, INRIA, and TNO. Note that three different vehicles with different architectures and different control can cooperate using the data exchanged and a common decision algorithm to conduct complex cooperative maneuvers. 1. The Industrial Automation Institute (IAI-CSIC) merged with the Polytechnic University of Madrid to become the Robotics and Automation Center (CAR-UPM-CSIC) on March 1, 2010.

The Grand Cooperative Driving Challenge 2016: boosting the introduction of cooperative automated vehicles

2016 ◽  
Vol 23 (4) ◽  
pp. 146-152 ◽  
Author(s):  
Cristofer Englund ◽  
Lei Chen ◽  
Jeroen Ploeg ◽  
Elham Semsar-Kazerooni ◽  
Alexey Voronov ◽  
...  
Keyword(s):  
Automated Vehicles ◽  
Cooperative Driving

scholarly journals Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jaehyun Jason So ◽  
Sungho Park ◽  
Jonghwa Kim ◽  
Jejin Park ◽  
Ilsoo Yun
Keyword(s):  
Driving Simulator ◽  
Road Traffic ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
The Road ◽  
Traffic Conditions ◽  
Vehicle System ◽  
Driver’S Characteristics

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

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