Pedestrian assessment: Is displaying automated driving mode in self-driving vehicles as relevant as emitting an engine sound in electric vehicles?

2021 ◽  
Vol 94 ◽  
pp. 103425
Author(s):  
Stefanie M. Faas ◽  
Martin Baumann
Keyword(s):  
Electric Vehicles ◽  
Automated Driving ◽  
Driving Mode

Assessing the Effect of Countdown Featured TOR Signal on Drivers in Automated Driving Mode Change

2020 ◽  
Vol 64 (1) ◽  
pp. 1213-1217
Author(s):  
HyunJoo Park ◽  
HyunJae Park ◽  
Sang-Hwan Kim
Keyword(s):  
Reaction Time ◽  
Situation Awareness ◽  
Time Preference ◽  
Automated Driving ◽  
Mode Change ◽  
Driving Mode

In conditional automated driving, drivers may be required starting manual driving from automated driving mode after take-over request (TOR). The objective of the study was to investigate different TOR features for drivers to engage in manual driving effectively in terms of reaction time, preference, and situation awareness (SA). Five TOR features, including four features using countdown, were designed and evaluated, consisted of combinations of different modalities and codes. Results revealed the use of non-verbal sound cue (beep) yielded shorter reaction time while participants preferred verbal sound cue (speech). Drivers' SA was not different for TOR features, but the level of SA was affected by different aspects of SA. The results may provide insights into designing multimodal TOR along with drivers' behavior during take-over tasks.

All-Climate Battery Technology for Electric Vehicles: Inching Closer to the Mainstream Adoption of Automated Driving

2019 ◽  
Vol 7 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Xiao-Guang Yang ◽  
Shanhai Ge ◽  
Ningning Wu ◽  
Yongzhi Mao ◽  
Fengchun Sun ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Automated Driving ◽  
Battery Technology

Lateral Motion Control of a Vehicle Using the Rear Steer: Simulation Study

2015 ◽  
Author(s):  
Jin-Woo Lee ◽  
Bakhtiar B. Litkouhi
Keyword(s):  
Motion Control ◽  
Steering System ◽  
Lateral Motion ◽  
Automated Driving ◽  
Motion Error ◽  
Driving Mode ◽  
Vehicle Technology ◽  
Path Prediction ◽  
Future Work ◽  
Control Methodology

The lateral motion control is a key element for automated driving vehicle technology. Typically, the front steering system has been used as the primary actuator for vehicle lateral motion control. Alternatively, this paper presents a new method of the lateral motion control using a rear steer. When combined with the front steer actuator, the rear steer can generate more dynamically responsive turning of the vehicle. In addition, the rear steer can be used as a secondary back up actuator when the front steer actuator fails to operate during automated driving mode. Similar to the prior research that has used the front steer actuator for the lateral control, the control methodology presented in this paper maintains the same hierarchical framework, i.e., sensor fusion, path prediction, path planning, and motion control. Since the rear steer is in play for the vehicle lateral motion control, the equations for the path prediction and vehicle dynamics are re-derived with non-zero front steer and rear steer angles. Combined with the rear steering dynamics, the model predictive control (MPC) technique is applied for motion error minimization. This paper describes the theoretical part of the algorithm, and provides simulation results to show effectiveness of the algorithm. Future work will include vehicle implementation, testing, and evaluation.

Off-Road Glance Behavior, Response Time to a Forward Collision Hazard, and Engagement Strategy Effects in Automated Driving

2017 ◽  
Vol 61 (1) ◽  
pp. 1909-1913 ◽  
Author(s):  
Mike Blommer ◽  
Reates Curry ◽  
Dev Kochhar ◽  
Rads Swaminathan ◽  
Walter Talamonti ◽  
...  
Keyword(s):  
Response Time ◽  
Automated Driving ◽  
Secondary Tasks ◽  
Collision Warning ◽  
Driving Mode ◽  
Driver Response ◽  
The Road ◽  
Behavior Response ◽  
The Face ◽  
Forward Collision Warning

Blommer et al. (2015) reported on a simulator study that investigated a driver engagement (DE) strategy designed to keep the driver-in-the-loop during automated driving in the face of two different types of secondary tasks. The method, first reported by Carsten et al. (2012), involved driving in fully automated driving mode for 6 minutes followed by 1 minute of manual driving, after which this fixed schedule was repeated several times throughout the drive. This scheduled strategy was compared to a reference condition in which different participants experienced continuous automated driving without interruptions. For each condition, some participants watched a video and others listened to the radio. All drives ended in automated driving mode with a surprise forward collision (FC) hazard to which the participant had to manually intervene. Compared to video watchers, radio listeners responded faster, looked to the road scene more, and they were more often looking forward at FC event onset. The DE strategy had no effect on radio listeners. In contrast, video watchers responded to the hazard more quickly with the scheduled strategy than without it. However, there was no reliable statistical difference between DE conditions in percent-eye-glance-time looking to the forward road scene during automated driving or in the number of drivers looking forward at FC event onset. This paper presents additional analyses of off-road eye glance behavior and finds no relationship between how long people were looking away prior to receiving a Forward Collision Warning (FCW) and driver response time (RT). About 95% of all video watching drivers glanced back to the road within 20 sec regardless of the automated driving condition. Approximately 85% of glances away from the road in the scheduled mitigation condition were 7 sec or less.

Design of coordinated control strategy during driving mode switching for parallel hybrid electric vehicles

2018 ◽  
Vol 41 (9) ◽  
pp. 2507-2520
Author(s):  
Jiangtao Fu ◽  
Shuzhong Song ◽  
Zhumu Fu ◽  
Jianwei Ma
Keyword(s):  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Coordinated Control ◽  
Mode Switching ◽  
Vehicle Performance ◽  
Driving Mode ◽  
Response Characteristics ◽  
Hybrid Electric

Hybrid electric vehicles (HEVs) require the power to drive the vehicle via a combination of internal combustion engine (ICE) and electric machine (EM). To improve the drivability, the smooth torque change during the driving mode switching is essential. This task can be achieved by using the coordinated control strategy. This paper presents a coordinated control strategy based on considering the different dynamic response characteristics of the ICE and the EM, which can effectively suppress the torque surge during the driving mode switching processes. The novelty lies in the proposed control is a motor active synchronization control strategy without clutch disengagement based on the mode switching classification. The coordinated control strategy is designed according to the classification of the driving modes. The objective is to minimize torque fluctuation and maintain or improve the driving performance of the vehicle. Results from the computer simulation demonstrate the effectiveness of this approach in reducing the torque surge without sacrificing vehicle performance.

Evaluating Driver Features for Cognitive Distraction Detection and Validation in Manual and Level 2 Automated Driving

2020 ◽  
pp. 001872082096414
Author(s):  
Shiyan Yang ◽  
Kyle M. Wilson ◽  
Trey Roady ◽  
Jonny Kuo ◽  
Michael G. Lenné
Keyword(s):  
Feature Selection ◽  
Domain Knowledge ◽  
Ground Truth ◽  
Road User ◽  
Large Set ◽  
Automated Driving ◽  
Driving Mode ◽  
Cognitive Distraction ◽  
Driver Monitoring ◽  
Level 2

Objective This study aimed to investigate the impacts of feature selection on driver cognitive distraction (CD) detection and validation in real-world nonautomated and Level 2 automated driving scenarios. Background Real-time driver state monitoring is critical to promote road user safety. Method Twenty-four participants were recruited to drive a Tesla Model S in manual and Autopilot modes on the highway while engaging in the N-back task. In each driving mode, CD was classified by the random forest algorithm built on three “hand-crafted” glance features (i.e., percent road center [PRC], the standard deviation of gaze pitch, and yaw angles), or through a large number of features that were transformed from the output of a driver monitoring system (DMS) and other sensing systems. Results In manual driving, the small set of glance features was as effective as the large set of machine-generated features in terms of classification accuracy. Whereas in Level 2 automated driving, both glance and vehicle features were less sensitive to CD. The glance features also revealed that the misclassified driver state was the result of the dynamic fluctuations and individual differences of cognitive loads under CD. Conclusion Glance metrics are critical for the detection and validation of CD in on-road driving. Applications The paper suggests the practical value of human factors domain knowledge in feature selection and ground truth validation for the development of driver monitoring technologies.

scholarly journals Forecast of the Impact of Electric Car Battery Performance and Infrastructural and Demographic Changes on Cumulative Accessibility for the Five Most Populous Cities in Poland

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8350
Author(s):  
Piotr Rosik ◽  
Sławomir Goliszek ◽  
Tomasz Komornicki ◽  
Patryk Duma
Keyword(s):  
Labor Markets ◽  
Electric Vehicles ◽  
Demographic Changes ◽  
Key Factors ◽  
Driving Mode ◽  
Suburban Areas ◽  
Electric Car ◽  
Technological Developments ◽  
Demographic Processes ◽  
The Impact

The purpose of this study is to compare (1) technological factors (the ranges offered by the batteries of three popular electric vehicles in Poland); (2) infrastructure improvements; and (3) demographic changes and their impact on accessibility in the context of the ranges of labor markets within the 30, 60, and 90 min isochrones in moderate driving mode for the five largest cities in Poland using cumulative accessibility. We conclude that technological developments result in a much greater improvement in accessibility than demographic and infrastructural change. This is already visible with the 30 to 60 min isochrones, in particular when using the BMW in Cracow (with a more than 36% improvement in accessibility). Even greater changes, reaching as much as over 90%, are observed for the 60–90 min isochrones. The analysis shows that the shift in electromobility may be constrained by parallel demographic processes, dispersion of population in suburban areas, and the development of road infrastructure. The novelty of the approach stems from the fact that it is based on three above mentioned key factors that influence the accessibility of labor markets for EV users in the largest cities up to 2030.

scholarly journals Perceived safety and trust in SAE Level 2 partially automated cars: Results from an online questionnaire

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260953
Author(s):  
Sina Nordhoff ◽  
Jork Stapel ◽  
Xiaolin He ◽  
Alexandre Gentner ◽  
Riender Happee
Keyword(s):  
Structural Equation ◽  
Task Engagement ◽  
Equation Modeling ◽  
Future Research ◽  
Automated Driving ◽  
Online Questionnaire ◽  
Perceived Safety ◽  
Driving Mode ◽  
Level 2 ◽  
Partial Automation

The present online study surveyed drivers of SAE Level 2 partially automated cars on automation use and attitudes towards automation. Respondents reported high levels of trust in their partially automated cars to maintain speed and distance to the car ahead (M = 4.41), and to feel safe most of the time (M = 4.22) on a scale from 1 to 5. Respondents indicated to always know when the car is in partially automated driving mode (M = 4.42), and to monitor the performance of their car most of the time (M = 4.34). A low rating was obtained for engaging in other activities while driving the partially automated car (M = 2.27). Partial automation did, however, increase reported engagement in secondary tasks that are already performed during manual driving (i.e., the proportion of respondents reporting to observe the landscape, use the phone for texting, navigation, music selection and calls, and eat during partially automated driving was higher in comparison to manual driving). Unsafe behaviour was rare with 1% of respondents indicating to rarely monitor the road, and another 1% to sleep during partially automated driving. Structural equation modeling revealed a strong, positive relationship between perceived safety and trust (β = 0.69, p = 0.001). Performance expectancy had the strongest effects on automation use, followed by driver engagement, trust, and non-driving related task engagement. Perceived safety interacted with automation use through trust. We recommend future research to evaluate the development of perceived safety and trust in time, and revisit the influence of driver engagement and non-driving related task engagement, which emerged as new constructs related to trust in partial automation.

scholarly journals Drivers’ Ability to Engage in a Non-Driving Related Task While in Automated Driving Mode in Real Traffic

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 221654-221668
Author(s):  
Maria Klingegard ◽  
Jonas Andersson ◽  
Azra Habibovic ◽  
Emma Nilsson ◽  
Annie Rydstrom
Keyword(s):  
Automated Driving ◽  
Driving Mode ◽  
Real Traffic

scholarly journals A Control Strategy for Driving Mode Switches of Plug-in Hybrid Electric Vehicles

2018 ◽  
Vol 10 (11) ◽  
pp. 4237 ◽  
Author(s):  
Yuping Zeng ◽  
Zhikai Huang ◽  
Yang Cai ◽  
Yonggang Liu ◽  
Yue Xiao ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Dynamic Characteristics ◽  
Duty Cycle ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Torque Ripple ◽  
Torque Control ◽  
Driving Mode ◽  
Wet Clutch ◽  
Hybrid Electric

Driving mode switches of hybrid vehicles are significant events. Due to the different dynamic characteristics of the engine, motor, and wet clutch, it is difficult to coordinate torque fluctuations caused by mode switches. This paper focused on a control strategy for driving mode switches of plug-in hybrid electric vehicles (PHEVs) with a multi-disk wet clutch. First, the dynamic model of the PHEV was established, and a rule-based control strategy was proposed to divide the working mode regions and distribute the torque between engine and motor. Second, the dual fuzzy control strategy for a wet clutch and the coordinated torque control strategy for driving mode switches were proposed. The dual fuzzy logic control system consisted of the initial pulse-width modulation (PWM)’s duty cycle control and the changing rate of the PWM’s duty cycle control. Considering the difference in the dynamic characteristics between engine, motor, and wet clutch, a coordinated control strategy for the driving mode switches of PHEVs was put forward. Third, simulations of driving mode switches between pure electric driving mode and only engine driving mode were conducted. The results showed that the proposed control strategy could reduce the torque ripple and the jerk of the vehicle, completely satisfying the requirements of China. Finally, the control strategy for the motor-assisted engine starting process was tested on the bench. The experiment results indicated that the proposed control strategy was effective.

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