Steering is initiated based on error accumulation.

Vehicle control by humans is possible because the central nervous system is capable of using visual information to produce complex sensorimotor actions. Drivers must monitor errors and initiate steering corrections of appropriate magnitude and timing to maintain a safe lane position. The perceptual mechanisms determining how a driver processes visual information and initiates steering corrections remain unclear. Previous research suggests 2 potential alternative mechanisms for responding to errors: (a) perceptual evidence (error) satisficing fixed constant thresholds (Threshold), or (b) the integration of perceptual evidence over time (Accumulator). To distinguish between these mechanisms, an experiment was conducted using a computer-generated steering correction paradigm. Drivers (N = 20) steered toward an intermittently appearing "road-line" that varied in position and orientation with respect to the driver's position and trajectory. One key prediction from a Threshold framework is a fixed absolute error response across conditions regardless of the rate of error development, whereas the Accumulator framework predicts that drivers would respond to larger absolute errors when the error signal develops at a faster rate. Results were consistent with an Accumulator framework; thus we propose that models of steering should integrate perceived control error over time in order to accurately capture human perceptual performance. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving.

OBJECTIVE: To present a structured, narrative review highlighting research into human perceptual-motor coordination that can be applied to automated vehicle (AV)-human transitions. BACKGROUND: Manual control of vehicles is made possible by the coordination of perceptual-motor behaviors (gaze and steering actions), where active feedback loops enable drivers to respond rapidly to ever-changing environments. AVs will change the nature of driving to periods of monitoring followed by the human driver taking over manual control. The impact of this change is currently poorly understood. METHOD: We outline an explanatory framework for understanding control transitions based on models of human steering control. This framework can be summarized as a perceptual-motor loop that requires (a) calibration and (b) gaze and steering coordination. A review of the current experimental literature on transitions is presented in the light of this framework. RESULTS: The success of transitions are often measured using reaction times, however, the perceptual-motor mechanisms underpinning steering quality remain relatively unexplored. CONCLUSION: Modeling the coordination of gaze and steering and the calibration of perceptual-motor control will be crucial to ensure safe and successful transitions out of automated driving. APPLICATION: This conclusion poses a challenge for future research on AV-human transitions. Future studies need to provide an understanding of human behavior that will be sufficient to capture the essential characteristics of drivers reengaging control of their vehicle. The proposed framework can provide a guide for investigating specific components of human control of steering and potential routes to improving manual control recovery.

Metacognitive judgements of perceptual-motor steering performance.

Control of skilled actions requires rapid information sampling and processing, which may largely be carried out subconsciously. However, individuals often need to make conscious strategic decisions that ideally would be based upon accurate knowledge of performance. Here, we determined the extent to which individuals have explicit awareness of their steering performance (conceptualised as "metacognition"). Participants steered in a virtual environment along a bending road while attempting to keep within a central demarcated target zone. Task demands were altered by manipulating locomotor speed (fast/slow) and the target zone (narrow/wide). All participants received continuous visual feedback about position in zone, and one sub-group was given additional auditory warnings when exiting/entering the zone. At the end of each trial, participants made a metacognitive evaluation: the proportion of the trial they believed was spent in the zone. Overall, although evaluations broadly shifted in line with task demands, participants showed limited calibration to performance. Regression analysis showed that evaluations were influenced by two components: (a) direct monitoring of performance and (b) indirect task heuristics estimating performance based on salient cues (e.g., speed). Evaluations often weighted indirect task heuristics inappropriately, but the additional auditory feedback improved evaluations seemingly by reducing this weighting. These results have important implications for all motor tasks where conscious cognitive control can be used to influence action selection.

The need for speed: global optic flow speed influences steering.

How do animals follow demarcated paths? Different species are sensitive to optic flow and one control solution is to maintain the balance of flow symmetry across visual fields; however, it is unclear whether animals are sensitive to changes in asymmetries when steering along curved paths. Flow asymmetries can alter the global properties of flow (i.e. flow speed) which may also influence steering control. We tested humans steering curved paths in a virtual environment. The scene was manipulated so that the ground plane to either side of the demarcated path produced larger or smaller asymmetries in optic flow. Independent of asymmetries and the locomotor speed, the scene properties were altered to produce either faster or slower globally averaged flow speeds. Results showed that rather than being influenced by changes in flow asymmetry, steering responded to global flow speed. We conclude that the human brain performs global averaging of flow speed from across the scene and uses this signal as an input for steering control. This finding is surprising since the demarcated path provided sufficient information to steer, whereas global flow speed (by itself) did not. To explain these findings, existing models of steering must be modified to include a new perceptual variable: namely global optic flow speed.

Optic flow asymmetries bias high-speed steering along roads.

How do animals and insects use visual information to move through the world successfully? Optic flow, the pattern of motion at the eye, is a powerful source of information about self-motion. Insects and humans are sensitive to the global pattern of optic flow and try to maintain flow symmetry when flying or walking. The environments humans encounter, however, often contain demarcated paths that constrain future trajectories (e.g., roads), and steering has been successfully modeled using only road edge information. Here we examine whether flow asymmetries from a textured ground plane influences humans steering along demarcated paths. Using a virtual reality simulator we observed that different textures on either side of the path caused predictable biases to steering trajectories, consistent with participants reducing flow asymmetries. We also generated conditions where one textured region had no flow (either the texture was removed or the textured region was static). Despite the presence of visible path information, participants were biased toward the no-flow region consistent with reducing flow asymmetries. We conclude that optic flow asymmetries can lead to biased locomotor steering even when traveling along demarcated paths.