Evaluation of Vehicle Ride Height Adjustments Using a Driving Simulator

Testing of vehicle design properties by car manufacturers is primarily performed on-road and is resource-intensive, involving costly physical prototypes and large time durations between evaluations of alternative designs. In this paper, the applicability of driving simulators for the virtual assessment of ride, steering and handling qualities was studied by manipulating vehicle air suspension ride height (RH) (ground clearance) and simulator motion platform (MP) workspace size. The evaluation was carried out on a high-friction normal road, routinely used for testing vehicle prototypes, modelled in a driving simulator, and using professional drivers. The results showed the differences between the RHs were subjectively distinguishable by the drivers in many of the vehicle attributes. Drivers found standard and low RHs more appropriate for the vehicle in terms of the steering and handling qualities, where their performance was deteriorated, such that the steering control effort was the highest in low RH. This indicated inconsistency between subjective preferences and objective performance and the need for alternative performance metrics to be defined for expert drivers. Moreover, an improvement in drivers’ performance was observed, with a reduction of steering control effort, in larger MP configurations

Evidence Accumulation Account of Human Operators' Decisions in Intermittent Control During Inverted Pendulum Balancing

Human operators often employ intermittent, discontinuous control strategies in a variety of tasks. A typical intermittent controller monitors control error and generates corrective action when the deviation of the controlled system from the desired state becomes too large to ignore. Most contemporary models of human intermittent control employ simple, threshold-based trigger mechanism to model the process of control activation. However, recent experimental studies demonstrate that the control activation patterns produced by human operators do not support threshold-based models, and provide evidence for more complex activation mechanisms. In this paper, we investigate whether intermittent control activation in humans can be modeled as a decision-making process. We utilize an established drift-diffusion model, which treats decision making as an evidence accumulation process, and study it in simple numerical simulations. We demonstrate that this model robustly replicates the control activation patterns (distributions of control error at movement onset) produced by human operators in previously conducted experiments on virtual inverted pendulum balancing. Our results provide support to the hypothesis that intermittent control activation in human operators can be treated as an evidence accumulation process

Using Driver Control Models to Understand and Evaluate Behavioral Validity of Driving Simulators

For a driving simulator to be a valid tool for research, vehicle development, or driver training, it is crucial that it elicits similar driver behavior as the corresponding real vehicle. To assess such behavioral validity, the use of quantitative driver models has been suggested but not previously reported. Here, a task-general conceptual driver model is proposed, along with a taxonomy defining levels of behavioral validity. Based on these theoretical concepts, it is argued that driver models without explicit representations of sensory or neuromuscular dynamics should be sufficient for a model-based assessment of driving simulators in most contexts. As a task-specific example, two parsimonious driver steering models of this nature are developed and tested on a dataset of real and simulated driving in near-limit, low-friction circumstances, indicating a clear preference of one model over the other. By means of closed-loop simulations, it is demonstrated that the parameters of this preferred model can generally be accurately estimated from unperturbed driver steering data, using a simple, open-loop fitting method, as long as the vehicle positioning data are reliable. Some recurring patterns between the two studied tasks are noted in how the model’s parameters, fitted to human steering, are affected by the presence or absence of steering torques and motion cues in the simulator

Comparing and validating models of driver steering behaviour in collision avoidance and vehicle stabilisation

A number of driver models were fitted to a large data set of human truck driving, from a simulated near-crash, low-friction scenario, yielding two main insights: steering to avoid a collision was best described as an open-loop manoeuvre of predetermined duration, but with situation-adapted amplitude, and subsequent vehicle stabilisation could to a large extent be accounted for by a simple yaw rate nulling control law. These two phenomena, which could be hypothesised to generalise to passenger car driving, were found to determine the ability of four driver models adopted from the literature to fit the human data. Based on the obtained results, it is argued that the concept of internal vehicle models may be less valuable when modelling driver behaviour in non-routine situations such as near-crashes, where behaviour may be better described as direct responses to salient perceptual cues. Some methodological issues in comparing and validating driver models are also discussed

Understanding Cue Utility in Controlled Evasive Driving Manoeuvres: Optimizing Vestibular Cues for Simulator & Human Abilities

Most daily driving tasks are of low bandwidth and therefore the relatively slow visual system receives enough cue information to perform the task in a manner that is statistically indistinguishable from reality. On the other hand, evasive maneuvers are of such a high bandwidth that waiting for the visual cues to change is too slow and skilled drivers use steering torques and vestibular motion cues to know how the car is responding in order to make rapid corrective actions. In this study we show for evasive maneuvers on snow and ice, for which we have real world data from skilled test drivers, that the choice of motion cuing algorithm (MCA) settings has a tremendous impact on the saliency of motion cues and their similarity with reality. We demonstrate this by introducing a novel optimization scheme to optimize the classic MCA in the context of an MCA-Simulator-Driver triplet of constraints. We incorporate the following four elements to tune the MCA for a particular maneuver: 1) acceleration profiles of the maneuver observed in reality, 2) vestibular motion perception model, 3) motion envelope constraints of the simulator, and 4) a set of heuristics extracted from the literature about human motion perception (i.e. coherence zones). Including these elements in the tuning process, notwithstanding the easiness of the tuning process, respects motion platform constraints and considers human perception. Moreover the inevitable phase and gain errors arising as a major consequence of MCA are always kept within the human coherence zones, and subsequently are not perceptible as false cues. It is expected that this approach to MCA tuning will increase the transfer of training from simulator to reality for evasive driving maneuvers where students need training most and are most dangerous to perform in reality

Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers

Objective We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers. Background Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes. On the other hand, laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. However, it is unclear whether the cognitive processes implicated in these tasks are as paramount to decisions that are ingrained in more complex behaviors, such as driving. Results The drivers’ probability of accepting the available gap increased with the size of the gap; importantly, response time increased with time gap but not distance gap. The generalized drift-diffusion model explained the observed decision outcomes and response time distributions, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions. Conclusion Our results suggest that dynamic evidence accumulation is an essential mechanism underlying left-turn gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help to understand human behavior in complex real-world tasks. Application Potential applications of our results include real-time prediction of human behavior by automated vehicles and simulating realistic human-like behaviors in virtual environments for automated vehicles

Defining interactions: a conceptual framework for understanding interactive behaviour in human and automated road traffic

Rapid advances in technology for highly automated vehicles (HAVs) have raised concerns about coexistence of HAVs and human road users. Although there is a long tradition of research into human road user interactions, there is a lack of shared models and terminology to support cross-disciplinary research and development towards safe and acceptable interaction-capable HAVs. Here, we review the main themes and findings in previous theoretical and empirical interaction research, and find large variability in perspectives and terminologies. We unify these perspectives in a structured, cross-theoretical conceptual framework, describing what road traffic interactions are, how they arise, and how they get resolved. Two key contributions are: (1) a stringent definition of “interaction”, as “a situation where the behaviour of at least two road users can be interpreted as being influenced by the possibility that they are both intending to occupy the same region of space at the same time in the near future”, and (2) a taxonomy of the types of behaviours that road users exhibit in interactions. We hope that this conceptual framework will be useful in the development of improved empirical methodology, theoretical models, and technical requirements on vehicle automation

Raw and processed fish show identical Listeria monocytogenes genotypes with pulsed-field gel electrophoresis

Reprinted with permission from the Journal of Food Protection. Copyright held by the International Association for Food Protection, Des Moines, Iowa, U.S.A

Modeling human road crossing decisions as reward maximization with visual perception limitations

Understanding the interaction between different road users is critical for road safety and automated vehicles (AVs). Existing mathematical models on this topic have been proposed based mostly on either cognitive or machine learning (ML) approaches. However, current cognitive models are incapable of simulating road user trajectories in general scenarios, and ML models lack a focus on the mechanisms generating the behavior and take a high-level perspective which can cause failures to capture important human-like behaviors. Here, we develop a model of human pedestrian crossing decisions based on computational rationality, an approach using deep reinforcement learning (RL) to learn boundedly optimal behavior policies given human constraints, in our case a model of the limited human visual system. We show that the proposed combined cognitive-RL model captures human-like patterns of gap acceptance and crossing initiation time. Interestingly, our model’s decisions are sensitive to not only the time gap, but also the speed of the approaching vehicle, something which has been described as a “bias” in human gap acceptance behavior. However, our results suggest that this is instead a rational adaption to human perceptual limitations. Moreover, we demonstrate an approach to accounting for individual differences in computational rationality models, by conditioning the RL policy on the parameters of the human constraints. Our results demonstrate the feasibility of generating more human-like road user behavior by combining RL with cognitive models