The Role of Attention in Increasingly Autonomous Driving

Trent is Senior Technical Leader Crash Avoidance at Volvo Cars Safety Centre, and Adjunct Professor at Chalmers University of Technology in Gothenburg, Sweden. At Volvo Cars his role is to identify and define future leading edge safety needs within crash avoidance. His role provides leadership in safety analysis and research, supporting development of safety requirements and strategies and to drive human related aspects of safety design in a broader perspective, using the possibilities with new active safety functions and systems. For example he is responsible for safety in Volvo\u27s self-driving car program Drive Me. At Chalmers, he is very active in the analysis of naturalistic driving data at SAFER Vehicle and Traffic Safety Centre at Chalmers, where he recently was PI for a SHRP2 S08 naturalistic driving risk analysis of driver inattention

Holistic assessment of driver assistance systems: how can systems be assessed with respect to how they impact glance behaviour and collision avoidance?

This study demonstrates the need for a holistic safety-impact assessment of an advanced driver assistance system (ADAS) and its effect on eye-glance behaviour. It implements a substantial incremental development of the what-if (counterfactual) simulation methodology, applied to rear-end crashes from the SHRP2 naturalistic driving data. This assessment combines (i) the impact of the change in drivers’ off-road glance behaviour due to the presence of the ADAS, and (ii) the safety impact of the ADAS alone. The results illustrate how the safety benefit of forward collision warning and autonomous emergency braking, in combination with adaptive cruise control (ACC) and driver assist (DA) systems, may almost completely dominate the safety impact of the longer off-road glances that activated ACC and DA systems may induce. Further, this effect is shown to be robust to induced system failures. The accuracy of these results is tempered by outlined limitations, which future estimations will benefit from addressing. On the whole, this study is a further step towards a successively more accurate holistic risk assessment which includes driver behavioural responses such as off-road glances together with the safety effects provided by the ADAS

Drivers anticipate lead-vehicle conflicts during automated longitudinal control: Sensory cues capture driver attention and promote appropriate and timely responses

Adaptive Cruise Control (ACC) has been shown to reduce the exposure to critical situations by maintaining a safe speed and headway. It has also been shown that drivers adapt their visual behavior in response to the driving task demand with ACC, anticipating an impending lead vehicle conflict by directing their eyes to the forward path before a situation becomes critical. The purpose of this paper is to identify the causes related to this anticipatory mechanism, by investigating drivers’ visual behavior while driving with ACC when a potential critical situation is encountered, identified as a forward collision warning (FCW) onset (including false positive warnings). This paper discusses how sensory cues capture attention to the forward path in anticipation of the FCW onset. The analysis used the naturalistic database EuroFOT to examine visual behavior with respect to two manually-coded metrics, glance location and glance eccentricity, and then related the findings to vehicle data (such as speed, acceleration, and radar information). Three sensory cues (longitudinal deceleration, looming, and brake lights) were found to be relevant for capturing driver attention and increase glances to the forward path in anticipation of the threat; the deceleration cue seems to be dominant. The results also show that the FCW acts as an effective attention-orienting mechanism when no threat anticipation is present. These findings, relevant to the study of automation, provide additional information about drivers’ response to potential lead-vehicle conflicts when longitudinal control is automated. Moreover, these results suggest that sensory cues are important for alerting drivers to an impending critical situation, allowing for a prompt reaction

A Bayesian reference model for visual time-sharing behaviour in manual and automated naturalistic driving

Visual time-sharing (VTS) behavior characterizes an inattentive driver. Because inattention has been identified as the major contributing factor in traffic crashes, understanding the relation between VTS and crash risk could help reduce crash risk through the development of inattention countermeasures. The aims of this study are 1) to develop a reference model of VTS behavior and 2) reveal if vehicle automation influences VTS behavior. The reference model was based on naturalistic eye-tracking data. VTS sequences were extracted from routine driving data (including manual and automated driving). We used Bayesian Generalized Linear Mixed Models for a range of on- and off-path glance-based metrics. Each parameter was estimated with a probability distribution and summarized with credible intervals containing the model parameters with 95% probability. The reference model corroborates previous findings from driving simulator experiments and on-road studies, but also captures the characteristics of on-path and off-path glance behavior in greater detail. The model demonstrated that 1) there was minimal change in VTS behavior due to automation, and 2) the percentage of time that glances fell on-path (PRC) was greater for all routine driving (~80%) than for VTS sequences (~50%). The PRC was the only metric that was sensitive to VTS, but it did not differentiate between manual and automated driving. Our model, by describing a measure of inattention (VTS behavior), can be used in future driver models to improve the computer simulations used to design ADASs and evaluate their safety benefits. Additionally, the model could serve as a detailed reference for inattention guidelines

Automation aftereffects: the influence of automation duration, test track and timings

Automation aftereffects (i.e., degraded manual driving performance, delayed responses, and more aggressive avoidance maneuvers) have been found in driving simulator studies. In addition, longer automation duration seems to result in more severe aftereffects, compared to shorter duration. The extent to which these findings generalize to real-world driving is currently unknown. The present study investigated how automation duration affects drivers\u27 take-over response quality and driving performance in a road-work zone. Seventeen participants followed a lead vehicle on test track. They encountered the road-work zone four times: two times while driving manually, and after a short and a long automation duration. The take-over request was issued before the lead vehicle changed lane to reveal the road-work zone. After both short and long automation durations, all drivers deactivated automation well ahead of the road-work zone. Compared to manual, drivers started their steering maneuvers earlier or at similar times after automation (independently of duration), and none of the drivers crashed. However, slight increases in vehicle speed and accelerations were observed after exposure to automation. In sum, the present study did not observe as large automation aftereffects on the test track as previously found in driving simulator studies. The extent to which these results are a consequence of a more realistic test environment, or due to the duration between the timings for the take-over request and the conflict appearance, is still unknown

Increasing diversity of production cell lines through miniaturization, automation, and high-throughput analytics

The development of a successful biologic therapeutic manufacturing process begins with the creation of a stable clonal cell line. Since attributes of the production cell line will significantly impact upstream and downstream processes, researchers must find ways to generate several candidate lines with diverse properties. However, a wide diversity is difficult to achieve since cultures are commonly selected, maintained, and screened as populations. In these populations, robust sub-populations can overtake the overall culture and reduce diversity. To combat this, sub-populations must be physically separated by splitting or subcloning, and maintained in individual vessels requiring intensive labor and infrastructure. As a result, researchers must balance between either increasing diversity vs. increasing resources need to maintain and screen hundreds of cultures. In order to shift this balance towards greater diversity, we have developed systems that combines miniaturization of culture vessels, targeted use of automation, and single cell analysis to allow for hundreds of cell lines to be isolated, maintained, and analyzed. We demonstrate cell lines can be easily maintained in simple low volume formats with no impact on cells. We show that we can significantly improve and maintain diversity through separation and isolation of hundreds of cultures. Additionally, higher throughputs allows to assess cell line phenotypes of multiple candidate lines early in development. Benefits achieved through this approach did not increase resources or timelines. Moving towards miniaturization combined with single cell analysis will also enable future possibilities for more precise cell engineering and gene editing

Detection and response to critical lead vehicle deceleration events with peripheral vision: Glance response times are independent of visual eccentricity

Studies show high correlations between drivers’ off-road glance duration or pattern and the frequency of crashes. Understanding drivers’ use of peripheral vision to detect and react to threats is essential to modelling driver behavior and, eventually, preventing crashes caused by visual distraction. A between-group experiment with 83 participants was conducted in a high-fidelity driving simulator. Each driver in the experiment was exposed to an unexpected, critical, lead vehicle deceleration, when performing a self-paced, visual-manual, tracking task at different horizontal visual eccentricity angles (12\ub0, 40\ub0 and 60\ub0). The effect of visual eccentricity on threat detection, glance and brake response times was analyzed. Contrary to expectations, the driver glance response time was found to be independent of the eccentricity angle of the secondary task. However, the brake response time increased with increasing task eccentricity, when measured from the driver’s gaze redirection to the forward roadway. High secondary task eccentricity was also associated with a low threat detection rate and drivers were predisposed to perform frequent on-road check glances while executing the task. These observations indicate that drivers use peripheral vision to collect evidence for braking during off-road glances. The insights will be used in extensions of existing driver models for virtual testing of critical longitudinal situations, to improve the representativeness of the simulation results

Driver conflict response during supervised automation: Do hands on wheel matter?

Securing appropriate driver responses to conflicts is essential in automation that is not perfect (because the driver is needed as a fall-back for system limitations and failures). However, this is recognized as a major challenge in the human factors literature. Moreover, in-depth knowledge is lacking regarding mechanisms affecting the driver response process. The first aim of this study was to investigate how driver conflict response while using highly reliable (but not perfect) supervised automation differ for drivers that (a) crash or avoid a conflict object and (b) report high trust or low trust in automation to avoid the conflict object. The second aim was to understand the influence on the driver conflict response of two specific factors: a hands-on-wheel requirement (with vs. without), and the conflict object type (garbage bag vs. stationary vehicle). Seventy-six participants drove with highly reliable but supervised automation for 30 minutes on a test track. Thereafter they needed to avoid a static object that was revealed by a lead-vehicle cut-out. The driver conflict response was assessed through the response process: timepoints for driver surprise reaction, hands-on-wheel, driver steering, and driver braking. Crashers generally responded later in all actions of the response process compared to non-crashers. In fact, some crashers collided with the conflict object without even putting their hands on the wheel. Driver conflict response was independent of the hands-on-wheel requirement. High-trust drivers generally responded later than the low-trust drivers or not at all, and only high trust drivers crashed. The larger stationary vehicle triggered an earlier surprise reaction compared to the garbage bag, while hands-on-wheel and steering response were similar for the two conflict object types. To conclude, crashing is associated with a delay in all actions of the response process. In addition, driver conflict response does not change with a hands-on-wheel requirement but changes with trust-level and conflict object type. Simply holding the hands on the wheel is not sufficient to prevent collisions or elicit earlier responses. High trust in automation is associated with late response and crashing, whereas low trust is associated with appropriate driver response

Interpreting Safety Outcomes: Waymo's Performance Evaluation in the Context of a Broader Determination of Safety Readiness

This paper frames recent publications from Waymo within the broader context of the safety readiness determination for an Automated Driving System (ADS). Starting from a brief overview of safety performance outcomes reported by Waymo (i.e., contact events experienced during fully autonomous operations), this paper highlights the need for a diversified approach to safety determination that complements the analysis of observed safety outcomes with other estimation techniques. Our discussion highlights: the presentation of a "credibility paradox" within the comparison between ADS crash data and human-derived baselines; the recognition of continuous confidence growth through in-use monitoring; and the need to supplement any aggregate statistical analysis with appropriate event-level reasoning

Analysis of Drivers\u27 Head and Eye Movement Correspondence: Predicting Drivers\u27 Glance Location Using Head Rotation Data

The relationship between a driver’s glance pattern and corresponding head rotation is not clearly defined. Head rotation and eye glance data drawn from a study conducted by the Virginia Tech Transportation Institute in support of methods development for the Strategic Highway Research Program (SHRP 2) naturalistic driving study were assessed. The data were utilized as input to classifiers that predicted glance allocation to the road and the center stack. A predictive accuracy of 83% was achieved with Hidden Markov Models. Results suggest that although there are individual differences in head-eye correspondence while driving, head-rotation data may be a useful predictor of glance location. Future work needs to investigate the correspondence across a wider range of individuals, traffic conditions, secondary tasks, and areas of interest