Analysis of Disengagements in Semi-Autonomous Vehicles: Drivers’ Takeover Performance and Operational Implications

This report analyzes the reactions of human drivers placed in simulated Autonomous Technology disengagement scenarios. The study was executed in a human-in-the-loop setting, within a high-fidelity integrated car simulator capable of handling both manual and autonomous driving. A population of 40 individuals was tested, with metrics for control takeover quantification given by: i) response times (considering inputs of steering, throttle, and braking); ii) vehicle drift from the lane centerline after takeover as well as overall (integral) drift over an S-turn curve compared to a baseline obtained in manual driving; and iii) accuracy metrics to quantify human factors associated with the simulation experiment. Independent variables considered for the study were the age of the driver, the speed at the time of disengagement, and the time at which the disengagement occurred (i.e., how long automation was engaged for). The study shows that changes in the vehicle speed significantly affect all the variables investigated, pointing to the importance of setting up thresholds for maximum operational speed of vehicles driven in autonomous mode when the human driver serves as back-up. The results shows that the establishment of an operational threshold could reduce the maximum drift and lead to better control during takeover, perhaps warranting a lower speed limit than conventional vehicles. With regards to the age variable, neither the response times analysis nor the drift analysis provide support for any claim to limit the age of drivers of semi-autonomous vehicles

Toward a Safer Transportation System for Senior Road Users

Senior pedestrians and drivers (65 years and older) are among the most vulnerable road users. As the population of seniors rise, concerns regarding older adults\u27 traffic safety are growing. The advantages of using autonomous vehicles, innovative vehicle technologies, and active transportation are becoming more widely recognized to improve seniors\u27 mobility and safety. This behooves researchers to further investigate senior road users’ safety challenges and countermeasures. This study contributes to the literature by achieving two main goals. First, to explore contributing factors affecting the safety of older pedestrians and drivers in the current transportation system. Second, to examine seniors’ perceptions, preferences, and behaviors toward autonomous vehicles and advanced vehicle technologies, the main components of future transportation systems. To achieve the first objective, crash data involving senior pedestrians and drivers were collected and analyzed. Using structural equation modeling, it was found out that seniors’ susceptibility to pedestrian incidents is a function of level of walking difficulty, fear of falling, and crossing evaluation capability. Senior drivers’ risk factors were found to be driving maneuver & crash location, road features & traffic control devices, driver condition & behavior, road geometric characteristics, crash time and lighting, road class latent factors, as well as pandemic variable. To achieve the second objective, a national survey and a driving simulator experiment were conducted among seniors. The national survey investigates seniors’ perceptions and attitudes to a wide range of AVs features from the perspective of pedestrians and users. Using principal component analysis and cluster analysis, three distinctive clusters of seniors were identified with different perceptions and attitude toward different AV options. The driving simulator experiment examined drivers’ behavior and preferences towards vehicle to infrastructure warning messages. Using the analysis of covariance technique, the results revealed that audio warning message was more effective compared to other scenarios. This finding is consistent with the results of stated preferences of the participants. Female and senior drivers had higher speed limit compliance rate. The findings of this study shed light on key aspects of the current and future of transportation systems that are needed to improve the safety of senior road users

GETTING ACTIVE WITH PASSIVE CROSSINGS: INVESTIGATING THE EFFICACY OF IN-VEHICLE AUDITORY ALERTS FOR RAIL ROAD CROSSINGS

Train-vehicle collisions at highway-rail grade crossings continue to be a major issue in the US and across the world. Installing additional hardware at individual crossings is expensive, time consuming, and potentially ineffective. To prevent recent trends in safety improvement from plateauing, experts are turning towards novel warning devices that can be applied to all crossings with minimal cost. In-vehicle auditory alerts (IVAAs) could potentially remedy many of the human factor issues related to crossing safety in a cost effective manner. This thesis presents a series of experiments designing and testing an IVAA system for grade level railroad (RR) crossings. Study 1 collected subjective data on a pool of potential in-vehicle auditory alerts from 31 undergraduate participants. The type of IVAAs was varied along a number of dimensions (pitch, repetition, wave shape, wording, voice, etc.). Results from study 1 were used to design a prototype IVAA crossing notification system. A pilot study was conducted to calibrate the simulated driving scenario featuring multiple RR crossings and a compliance behavior coding procedure. Compliance behavior was operationalized as an amount of visual scanning and pedal depression. Study 2 recruited 20 undergraduate participants to drive in a medium fidelity driving simulator featuring four types of RR crossings with and without IVAAs. Results suggest that IVAAs not only inform and remind drivers of how to comply at RR crossings, but also have a lasting effect on driver behavior after the IVAA is no longer presented. Compliance scores were highest among novel RR crossing visual warnings such as crossbucks featuring STOP or YIELD signs. Compliance was lowest for crossbucks alone and active gates in the off position. IVAAs had the largest impact on compliance scores at crossbucks and gates. The discussion includes implications for designing IVAA systems for RR crossings, and the potential implementation of prototype systems as a smartphone application

Investigation of smart work zone technologies using mixed simulator and field studies

Safety is the top concern in transportation, especially in work zones, as work zones deviate from regular driving environment and driver behavior is very different. In order to protect workers and create a safer work zone environment, new technologies are proposed by agencies and deployed to work zones, however, some are without scientific study before deployment. Therefore, quantitative studies need to be conducted to show the effectiveness of technologies. Driving simulator is a safe and cost-effective way to test effectiveness of new designs and compare different configurations. Field study is another scientific way of testing, as it provides absolute validity, while simulator study provides relative validity. The synergy of field and simulator studies construct a precise experiment as field study calibrates simulator design and validates simulator results. Two main projects, Evaluation of Automated Flagger Assistance Devices (AFADs), and Evaluation of Green Lights on Truck-Mounted Attenuator (TMA), are discussed in this dissertation to illustrate the investigation of smart work zone technologies using mixed simulator and field studies, along with one simulator project investigating interaction between human driven car and autonomous truck platoon in work zones. Both field and simulator studies indicated that AFADs improved stationary work zone safety by enhancing visibility, isolating workers from immediate traffic, and conveying clear guidance message to traffic. The results of green light on TMAs implied an inverse relationship between visibility/awareness of work zone and arrow board recognition/easy on eyes, but did not show if any of the light configurations is superior. Results anticipated for autonomous truck platoon in work zones are drivers behave more uniformly after being educated about the meaning of signage displayed on the back of truck, and performance measured with signage would be more preferable than those without signage. Applications of statistics are extension of studies, including experimental design, survey design, and data analysis. Data obtained from AFAD and Green Light projects were utilized to illustrate the methodologies of data analysis and model building, which incorporated simulator data, biofeedback and survey response to interpret the relationship among driver perspective and mental status, and driving behavior. From the studies conducted, it could be concluded that mixed simulator and field study is a good fit for smart work zone technologies investigation. Simulators provide a safe environment, flexibility and cost-effectiveness, while field studies calibrate and validate simulator setup and its results. The collaboration of two forms of study generates legitimate and convincing results for investigations. Applying statistical methodologies into transportation simulator and field studies is a good way to make experiment and survey design more rational, and the statistical methods are applicable for further data analysis.Includes bibliographical reference

Analysis of SHRP2 Data to Understand Normal and Abnormal Driving Behavior in Work Zones

This research project used the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study(NDS) to improve highway safety by using statistical descriptions of normal driving behavior to identify abnormal driving behaviors in work zones. SHRP2 data used in these analyses included 50 safety-critical events (SCEs) from work zones and 444 baseline events selected on a matched case-control design.Principal components analysis (PCA) was used to summarize kinematic data into “normal” and “abnormal”driving. Each second of driving is described by one point in three-dimensional principal component (PC) space;an ellipse containing the bulk of baseline points is considered “normal” driving. Driving segments without-of-ellipse points have a higher probability of being an SCE. Matched case-control analysis indicates that thespecific individual and traffic flow made approximately equal contributions to predicting out-of-ellipse driving.Structural Topics Modeling (STM) was used to analyze complex categorical data obtained from annotated videos.The STM method finds “words” representing categorical data variables that occur together in many events and describes these associations as “topics.” STM then associates topics with either baselines or SCEs. The STM produced 10 topics: 3 associated with SCEs, 5 associated with baselines, and 2 that were neutral. Distractionoccurs in both baselines and SCEs.Both approaches identify the role of individual drivers in producing situations where SCEs might arise. A countermeasure could use the PC calculation to indicate impending issues or specific drivers who may havehigher crash risk, but not to employ significant interventions such as automatically braking a vehicle without-of-ellipse driving patterns. STM results suggest communication to drivers or placing compliant vehicles in thetraffic stream would be effective. Finally, driver distraction in work zones should be discouraged

Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload

Explanations given by automation are often used to promote automation adoption. However, it remains unclear whether explanations promote acceptance of automated vehicles (AVs). In this study, we conducted a within-subject experiment in a driving simulator with 32 participants, using four different conditions. The four conditions included: (1) no explanation, (2) explanation given before or (3) after the AV acted and (4) the option for the driver to approve or disapprove the AV's action after hearing the explanation. We examined four AV outcomes: trust, preference for AV, anxiety and mental workload. Results suggest that explanations provided before an AV acted were associated with higher trust in and preference for the AV, but there was no difference in anxiety and workload. These results have important implications for the adoption of AVs.Comment: 42 pages, 5 figures, 3 Table

Improving Drivers’ Behaviour When Partial Driving Automation Fails

With the advent of automated vehicle systems, the role of drivers has changed to a more supervisory role. However, it is known that all vehicles with Level 2 (L2) systems have a very specific operational design domain (ODD) and can only function on limited conditions. Hence, it is important for drivers to perceive the situations properly and regain the control from the L2 system when needed. As suggested by past research, designing an informative interface could help drivers in their new supervision and intervention role while driving with L2 vehicles by providing feedback to drivers when hazards or event that may cause system failure are detected. On the other hand there are many situations where these vehicles cannot detect hazards and provide any feedback prior to the event. In these cases, training programs which provide drivers with an experience of these system limitations and allow them to practice dealing with such limitations can prove to be effective countermeasures. The objective of the current study is to employ different methods (designing HMI and training drivers) to increase drivers’ situational awareness regarding operational design domain (ODD) and improve drivers performance in transfer of control situations while driving with level 2 (L2) automation features. This study includes two experiments- in first experiment, an informative dashboard interface was designed and tested through three phases (observation, prototyping, testing). Results from the testing phase showed that drivers who received the newly designed dashboards took back control more effectively and had more situational awareness compared to the control group. In the second experiment, a PC-based training program was designed and tested to improve drivers takeover response and situational awareness when L2 systems reach their ODD limits. Results showed drivers in the PC-based training group took back control more effectively when L2 systems reached their ODD limits and had more situational awareness compared to the drivers who received user manual or placebo training