The Influence of Lead Vehicle Behavior and Vehicle Rates of Closure on a Driver's Braking Behavior

2007 ◽  
Author(s):  
Nicholas J. Kelling ◽  
Gregory M. Corso
Keyword(s):  
Lead Vehicle

scholarly journals A NEW CAR-FOLLOWING MODEL CONSIDERING ACCELERATION OF LEAD VEHICLE

Transport ◽  
2014 ◽  
Vol 31 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Bingrong Sun ◽  
Na Wu ◽  
Ying-En Ge ◽  
Taewan Kim ◽  
Hongjun Michael Zhang
Keyword(s):  
General Motors ◽  
Weighted Sum ◽  
Relative Speed ◽  
New Model ◽  
Car Following ◽  
Stimulus Response ◽  
The Road ◽  
Car Following Model ◽  
On The Road ◽  
Lead Vehicle

For decades, the general motors (gm) car following model has received a great deal of attention and provided a basic framework to describe the interactions between vehicles on the road. It is based on the stimulus-response assumption that the following vehicle responds to the relative speed between the lead vehicle and itself. However, some of the empirical findings show that the assumption of gm model is not always true and need some modification. For example, the acceleration of the following vehicle is very sensitive to the sign of the relative speed and because of no term in the model that directly represents the leader’s acceleration, the follower’s response to the leader’s acceleration can be retarded. This paper offers a new car-following model that can be considered as a variant of the gm model that can better capture car following behavior. The new model treats the follower’s acceleration as a proportion of a weighted sum of the leader’s acceleration and the relative speed between the lead and following vehicles. This paper compares the new model with the original gm model numerically and the characteristics of the new parameters in the model are investigated. It is also shown that the new model overcomes the shortcomings of the original gm model identified in this paper and gives us more instruments to capture the real-world car-following behavior.

scholarly journals Do drivers change their manual car-following behaviour after automated car-following?

2020 ◽  
Author(s):  
Tyron Louw ◽  
Rafael Goncalves ◽  
Guilhermina Torrao ◽  
Vishnu Radhakrishnan ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Cruise Control ◽  
Automated Driving ◽  
Behavioural Adaptation ◽  
Car Following ◽  
Time Headway ◽  
Driving Behaviour ◽  
Driving Assistance ◽  
Change In Mean ◽  
Lead Vehicle

There is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control.

scholarly journals Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel

2018 ◽  
Vol 60 (8) ◽  
pp. 1095-1116 ◽  
Author(s):  
Trent W. Victor ◽  
Emma Tivesten ◽  
Pär Gustavsson ◽  
Joel Johansson ◽  
Fredrik Sangberg ◽  
...  
Keyword(s):  
Human Factors ◽  
Mental Model ◽  
Assistance System ◽  
Test Track ◽  
Incorrect Prediction ◽  
Hands On ◽  
Conflict Intervention ◽  
Lead Vehicle ◽  
Cognitive Understanding

Objective: The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation. Background: Securing driver engagement—by mitigating irony of automation (i.e., the better the automation, the less attention drivers will pay to traffic and the system, and the less capable they will be to resume control) and by communicating system limitations to avoid mental model misconceptions—is a major challenge in the human factors literature. Method: One hundred six drivers participated in three test-track experiments in which we studied driver intervention response to conflicts after driving highly reliable but supervised automation. After 30 min, a conflict occurred wherein the lead vehicle cut out of lane to reveal a conflict object in the form of either a stationary car or a garbage bag. Results: Supervision reminders effectively maintained drivers’ eyes on path and hands on wheel. However, neither these reminders nor explicit instructions on system limitations and supervision responsibilities prevented 28% (21/76) of drivers from crashing with their eyes on the conflict object (car or bag). Conclusion: The results uncover the important role of expectation mismatches, showing that a key component of driver engagement is cognitive (understanding the need for action), rather than purely visual (looking at the threat), or having hands on wheel. Application: Automation needs to be designed either so that it does not rely on the driver or so that the driver unmistakably understands that it is an assistance system that needs an active driver to lead and share control.

Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

2021 ◽  
Author(s):  
Vishnu Radhakrishnan ◽  
Natasha Merat ◽  
Tyron Louw ◽  
Rafael Goncalves ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Electrodermal Activity ◽  
Automated Driving ◽  
Physiological Indicators ◽  
Car Following ◽  
Time Headway ◽  
Driver Workload ◽  
Driving Task ◽  
Vehicle Automation ◽  
Lead Vehicle

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

Influence of Taillight Brightness on the Ability to Recognize Closing Speed, Closing Distance, and Closing vs. Separating

2018 ◽  
Vol 62 (1) ◽  
pp. 1868-1872
Author(s):  
Swaroop Dinakar ◽  
Jeffrey W. Muttart ◽  
Teena Garrison ◽  
Suntasy Gernhard ◽  
Jim Marr
Keyword(s):  
United States ◽  
Human Error ◽  
Commercial Vehicle ◽  
The United States ◽  
Large Percentage ◽  
Single Type ◽  
Moving Vehicle ◽  
Video Clips ◽  
Slow Moving ◽  
Lead Vehicle

Rear-end crashes contribute to a large percentage of fatal collisions in the United States. However, every rear-end collision cannot be classified as a single type of crash. Some crashes may be caused due to human error while some crashes may be attributed to a human inability to recognize closing speed well. Observers were shown two 4-second video clips of a commercial vehicle closing on a slow-moving vehicle on an unlit highway. The lead vehicle was depicted at distances of 91m (300 ft), 128m (420 ft) and 152m (500 ft). Closing speeds of 40 km/h (25 mph) and 105 km/h (65 mph) were depicted. The taillights on the lead vehicle were randomly shown as bright, or 80% dimmer which is typical of older taillights or aged retroreflective materials. Results showed that observers’ ability to recognize closing from separating worsened with increased distance, dimmer taillights and lower closing speeds. Observers perceived brighter taillights to be closer. Also, at greater distances, observers did not recognize closing speeds as well.

A Multirate, Multiscale Economic Model Predictive Control Approach for Velocity Trajectory Optimization of a Heavy Duty Truck

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Christian Earnhardt ◽  
Ben Groelke ◽  
John Borek ◽  
Mohammad Naghnaeian ◽  
Chris Vermillion
Keyword(s):  
Predictive Control ◽  
Economic Model ◽  
Heavy Duty ◽  
Time Step ◽  
Length Scales ◽  
Fuel Savings ◽  
Heavy Duty Truck ◽  
Economic Model Predictive Control ◽  
Road Grade ◽  
Lead Vehicle

Abstract This paper introduces a hierarchical economic model predictive control (MPC) approach for maximizing the fuel economy of a heavy-duty truck, which simultaneously accounts for aggregate terrain changes that occur over very long length scales, fine terrain changes that occur over shorter length scales, and lead vehicle behavior that can vary over much shorter time/length scales. To accommodate such disparate time and length scales, the proposed approach uses a multilayer MPC approach wherein the upper-level MPC uses a long distance step, a long time-step, and coarse discretization to account for the slower changes in road grade, while the lower-level MPC uses a shorter time-step to account for fine variations in road grade and rapidly changing lead vehicle behavior. The benefit of this multirate, multiscale approach is that the lower-level MPC leverages the upper-level's sufficiently long look-ahead while allowing for safe vehicle following and adjustment to fine road grade variations. The proposed strategy has been evaluated over four real-world road profiles in both open-highway and traffic environments, using a medium-fidelity simulink model furnished by Volvo Group North America. Compared with a conventional cruise control system plus vehicle following controller as a baseline, results show 4–5% fuel savings in an open highway setting and 6–8% fuel savings in the presence of traffic, without compromising trip time.

Analysis of a Novel Command Governor-Based Adaptive Cruise Controller for Non-Cooperative Vehicle Following

2019 ◽  
Author(s):  
Ben Groelke ◽  
Christian Earnhardt ◽  
John Borek ◽  
Chris Vermillion
Keyword(s):  
Prediction Models ◽  
Buffer Zone ◽  
Traffic Data ◽  
Cruise Control ◽  
Prediction Horizon ◽  
Trip Time ◽  
Vehicle Velocity ◽  
Wheel Torque ◽  
Different Levels ◽  
Lead Vehicle

Abstract This paper presents a novel adaptive cruise control (ACC) strategy that utilizes a command governor (CG) to enforce vehicle following constraints. The CG formulation relies on knowledge of the maximum possible braking deceleration of the lead vehicle and a tunable assumption regarding the lead vehicle velocity profile (offering different levels of conservatism) to modify wheel torque commands to ensure safe following. In particular, a safe following distance is defined as one in which the ego vehicle can avoid collision with the lead vehicle and maintain a sufficient following distance in the event that the lead vehicle exerts maximum braking deceleration. The CG seeks to adjust the wheel torque command such that the aforementioned constraint is satisfied at every step in a prediction horizon (i.e., at every step, if the lead vehicle exerts maximum braking deceleration, the ego vehicle can brake and remain outside of the aforementioned buffer zone), which requires an estimate of future lead vehicle behavior. In this work, we explore different levels of conservatism with regard to this assumption. Simulations are presented for a heavy-duty truck, using a stochastic lead vehicle model that has been calibrated with actual traffic data. Even for the most conservative lead vehicle prediction models, results show that this CG-based ACC strategy can reduce braking energy expended (used as a surrogate for fuel wasted) by up to 78%, while improving drivability and reducing total trip time.

scholarly journals Towards Self-Aware Multirotor Formations

Computers ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 7 ◽  
Author(s):  
Dennis Kaiser ◽  
Veronika Lesch ◽  
Julian Rothe ◽  
Michael Strohmeier ◽  
Florian Spieß ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Dynamic Behavior ◽  
The Self ◽  
General Category ◽  
Self Awareness ◽  
Aerial Vehicles ◽  
Air Space ◽  
Multiple Scenarios ◽  
Lead Vehicle ◽  
The Way

In the present day, unmanned aerial vehicles become seemingly more popular every year, but, without regulation of the increasing number of these vehicles, the air space could become chaotic and uncontrollable. In this work, a framework is proposed to combine self-aware computing with multirotor formations to address this problem. The self-awareness is envisioned to improve the dynamic behavior of multirotors. The formation scheme that is implemented is called platooning, which arranges vehicles in a string behind the lead vehicle and is proposed to bring order into chaotic air space. Since multirotors define a general category of unmanned aerial vehicles, the focus of this thesis are quadcopters, platforms with four rotors. A modification for the LRA-M self-awareness loop is proposed and named Platooning Awareness. The implemented framework is able to offer two flight modes that enable waypoint following and the self-awareness module to find a path through scenarios, where obstacles are present on the way, onto a goal position. The evaluation of this work shows that the proposed framework is able to use self-awareness to learn about its environment, avoid obstacles, and can successfully move a platoon of drones through multiple scenarios.

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