Approximating safe spacing policies for adaptive cruise control strategies

Willigen, W. H. van; Schut, Martijn C.; Kester, Leon

doi:10.1109/vnc.2011.6117118

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…Their simulation results show that small disturbances are damped through the platoon. And in [63], the uncertainties in the communication network and sensor information were studied. The uncertainty parameters and delays were approximated to be Gaussian distributed, and this model was used to calculate the minimal time headway for safety.…”

Section: Vehicle Longitude Dynamicsmentioning

confidence: 99%

Reliable and Efficient Broadcast Routing Using Multipoint Relays Over VANET For Vehicle Platooning

Wang¹,

Lim²

2022

Preprint

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In this paper, we design and implement a reliable broadcast algorithm over a VANET for supporting multi-hop forwarding of vehicle sensor and control packets that will enable vehicles to platoon with each other in order to form a road train behind the lead truck. In particular, we use multipoint relays (MPRs) for packet transmission, which leads to more efficient communication in a VANET. We evaluate the performance based on simulation by running a platooning simulation application program, and show that with MPRs, the communication in the VANET to form a road train is more efficient and reliable.

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Section: Vehicle Longitude Dynamicsmentioning

confidence: 99%

Reliable and Efficient Broadcast Routing Using Multipoint Relays Over VANET For Vehicle Platooning

Wang¹,

Lim²

2022

Preprint

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“…Shladover et al (2010) identified that drivers maintain 2.2 s, 1.6 s, and 1.1 s time gaps for 31.1%, 18.5%, and 50.4% of their vehicle-following time in ACC mode, respectively, and drivers maintain 0.6 s, 0.7 s, 0.9 s, and 1.1 s time gaps for 57%, 24%, 7%, and 12% of their vehicle-following time in CACC mode, respectively [24]. Willigen et al (2011) recommended a distance headway and a time headway for each platoon size (i.e., 20 and 30) and operating mode (i.e., ACC, CACC with transmitted accelerations, and CACC with estimated accelerations) (see Table 8) [25]. Horiguchi and Oguchi (2014) calculated distance gap for vehicles in CACC mode based on minimum safe distance gap, follower's speed, leader's speed, maximum acceleration, and maximum deceleration [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Longitudinal Control for Connected and Automated Vehicles in Contested Environments

2021

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The Society of Automotive Engineers (SAE) defines six levels of driving automation, ranging from Level 0 to Level 5. Automated driving systems perform entire dynamic driving tasks for Levels 3–5 automated vehicles. Delegating dynamic driving tasks from driver to automated driving systems can eliminate crashes attributed to driver errors. Sharing status, sharing intent, seeking agreement, or sharing prescriptive information between road users and vehicles dedicated to automated driving systems can further enhance dynamic driving task performance, safety, and traffic operations. Extensive simulation is required to reduce operating costs and achieve an acceptable risk level before testing cooperative automated driving systems in laboratory environments, test tracks, or public roads. Cooperative automated driving systems can be simulated using a vehicle dynamics simulation tool (e.g., CarMaker and CarSim) or a traffic microsimulation tool (e.g., Vissim and Aimsun). Vehicle dynamics simulation tools are mainly used for verification and validation purposes on a small scale, while traffic microsimulation tools are mainly used for verification purposes on a large scale. Vehicle dynamics simulation tools can simulate longitudinal, lateral, and vertical dynamics for only a few vehicles in each scenario (e.g., up to ten vehicles in CarMaker and up to twenty vehicles in CarSim). Conventional traffic microsimulation tools can simulate vehicle-following, lane-changing, and gap-acceptance behaviors for many vehicles in each scenario without simulating vehicle powertrain. Vehicle dynamics simulation tools are more compute-intensive but more accurate than traffic microsimulation tools. Due to software architecture or computing power limitations, simplifying assumptions underlying convectional traffic microsimulation tools may have been a necessary compromise long ago. There is, therefore, a need for a simulation tool to optimize computational complexity and accuracy to simulate many vehicles in each scenario with reasonable accuracy. This research proposes a traffic microsimulation tool that employs a simplified vehicle powertrain model and a model-based fault detection method to simulate many vehicles with reasonable accuracy at each simulation time step under noise and unknown inputs. Our traffic microsimulation tool considers driver characteristics, vehicle model, grade, pavement conditions, operating mode, vehicle-to-vehicle communication vulnerabilities, and traffic conditions to estimate longitudinal control variables with reasonable accuracy at each simulation time step for many conventional vehicles, vehicles dedicated to automated driving systems, and vehicles equipped with cooperative automated driving systems. Proposed vehicle-following model and longitudinal control functions are verified for fourteen vehicle models, operating in manual, automated, and cooperative automated modes over two driving schedules under three malicious fault magnitudes on transmitted accelerations.

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“…They showed that the system was capable of damping small disturbances throughout the platoon. The uncertainties in communication network and sensor information were modeled by a Gaussian distribution in [18], which was applied to calculate the minimal time headway for safety reasons. Qin et al [19] studied the effects of stochastic delays on the dynamics of connected vehicles by analyzing the mean dynamics.…”

Section: Introductionmentioning

confidence: 99%

Developing a Distributed Consensus-Based Cooperative Adaptive Cruise Control System for Heterogeneous Vehicles with Predecessor Following Topology

Wang

Barth

2017

Journal of Advanced Transportation

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Connected and automated vehicle (CAV) has become an increasingly popular topic recently. As an application, Cooperative Adaptive Cruise Control (CACC) systems are of high interest, allowing CAVs to communicate with each other and coordinating their maneuvers to form platoons, where one vehicle follows another with a constant velocity and/or time headway. In this study, we propose a novel CACC system, where distributed consensus algorithm and protocol are designed for platoon formation, merging maneuvers, and splitting maneuvers. Predecessor following information flow topology is adopted for the system, where each vehicle only communicates with its following vehicle to reach consensus of the whole platoon, making the vehicle-to-vehicle (V2V) communication fast and accurate. Moreover, different from most studies assuming the type and dynamics of all the vehicles in a platoon to be homogenous, we take into account the length, location of GPS antenna on vehicle, and braking performance of different vehicles. A simulation study has been conducted under scenarios including normal platoon formation, platoon restoration from disturbances, and merging and splitting maneuvers. We have also carried out a sensitivity analysis on the distributed consensus algorithm, investigating the effect of the damping gain on convergence rate, driving comfort, and driving safety of the system.

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Approximating safe spacing policies for adaptive cruise control strategies

Cited by 9 publications

References 16 publications

Reliable and Efficient Broadcast Routing Using Multipoint Relays Over VANET For Vehicle Platooning

Reliable and Efficient Broadcast Routing Using Multipoint Relays Over VANET For Vehicle Platooning

Longitudinal Control for Connected and Automated Vehicles in Contested Environments

Developing a Distributed Consensus-Based Cooperative Adaptive Cruise Control System for Heterogeneous Vehicles with Predecessor Following Topology

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