Analysis of the effects of adaptive cruise control on driver behavior and awareness using a driving simulator

Kummetha, Vishal C.; Kondyli, Alexandra; Schrock, Steven D

doi:10.1080/19439962.2018.1518359

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…Imagine a pyramid, with Level 0 at the base (smallest size) and Level 5 at the peak (largest size). Each level adds functionalities and complexities, reflected in the increasing size of the pyramid [69].…”

Section: Stat2: Parameters For Ai Models (Trucks Vs Cars)mentioning

confidence: 99%

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Garikapati,

Shetiya

2024

BDCC

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The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects.

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Section: Stat2: Parameters For Ai Models (Trucks Vs Cars)mentioning

confidence: 99%

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Garikapati,

Shetiya

2024

BDCC

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“…The term "adaptive", in fact, is expressed the main feature of the system, which the ability to vary the speed according to the traffic conditions; it, in fact, through sensors RADAR or LIDAR frontal, can accelerate and decelerate automatically depending on the vehicle detection in front. In recent years, although the widespread use of this type of device has led to various research, none has been able to define the driver-vehicle interaction considering the different reaction times between the on and off the system, considering, therefore, the actual behavioral variation on the driving behavior [22,23].…”

Section: Impact Of Acc On Traffic Flowmentioning

confidence: 99%

Traffic Flow with the Adaptive Cruise Control: The Comparison between Autonomous and Manual Driving

Acerra¹,

Brasile²,

Simone³

et al. 2023

Preprint

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Adaptive Cruise Control (ACC) is useful in the most dangerous maneuvers such as braking and acceleration. This study assesses how ACC modifies traffic flows by analysing the differences be-tween manual and autonomous driving in connected and autonomous vehicles (CAVs). Using a platoon of 80 vehicles, tested in pairs on the road, it was possible to define the speed trends during braking and acceleration and the reaction times to the driving maneuvers (PRT, TH, TCC) with a kinematic data detector. The interactions between the CAV and the driver, have been studied in-novatively, i.e through gaze analysis. Situations of potential danger, characterized by the braking of the vehicle that precedes the car with the driver equipped with the eye tracker tool, have been recreated, considering the influence of the driver’s ACC experience. Results statistically confirmed that with the ACC switched on the reaction times are greater than manual driving (2.4/3.8 sec); this can lead to a reduction in road safety, further motivated by the rapid decrease in speed. The interpolation between automated and human data, finally, has allowed the detection of some criticalities of the system that are fundamental in order to reach the second level of automation.

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“…ACC provides support to drivers by adjusting vehicle speed to maintain a preset time headway that by design is within a reasonable safety margin. Indeed, previous studies have shown that when ACC is engaged, time headway increases [19][20][21][22] and consequently the frequency of close following events decreases [20,23,24]. Although previous research has highlighted decreased visual attention to the road ahead when drivers are using ACC, the overall safety effect when taking increased time headway and earlier brake onset into account has been deemed positive [22,[25][26][27].…”

Section: Introductionmentioning

confidence: 99%

In-vehicle nudging for increased Adaptive Cruise Control use: a field study

Gustavsson,

Ljung Aust

2024

J Multimodal User Interfaces

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Close following to lead vehicles is associated with increased risk of rear-end crashes in road traffic. One way to reduce instances of close following is through increased use of the Advanced Driver Assistance System (ADAS) Adaptive Cruise Control (ACC), which is designed to adjust vehicle speed to maintain a safe time headway. Since the activation of ACC is driver-initiated, there is a need to influence the propensity of drivers to use the function. This research aimed to explore whether in-vehicle nudging interventions could be effective for this purpose. A field trial was conducted to consecutively assess the effects of two nudges on drivers’ utilization of ACC, compared to baseline usage. Exposing the participants (n = 49) to the first ambient design nudge resulted in a 46% increase in ACC usage on average. Following the introduction of the second nudge (a competitive leaderboard nudge), the average increase among participants (n = 48) during the complete treatment period reached 61%. The changes in ACC utilization varied between individual drivers, highlighting the need to monitor behavioral outcomes of nudges and adapt them when needed. In conclusion, this research shows that utilizing in-vehicle nudging is a promising approach to increase the use of vehicle functions contributing to improved traffic safety.

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Analysis of the effects of adaptive cruise control on driver behavior and awareness using a driving simulator

Cited by 9 publications

References 14 publications

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Traffic Flow with the Adaptive Cruise Control: The Comparison between Autonomous and Manual Driving

In-vehicle nudging for increased Adaptive Cruise Control use: a field study

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