Driver's turning intent recognition model based on brain activation and contextual information

Traffic situations like turning at intersections are destined for safety-critical situations and accidents. Human errors are one of the main reasons for accidents in these situations. A model that recognizes the driver's turning intent could help to reduce accidents by warning the driver or stopping the vehicle before a dangerous turning maneuver. Most models that aim at predicting the probability of a driver's turning intent use only contextual information, such as gap size or waiting time. The objective of this study is to investigate whether the combination of context information and brain activation measurements enhances the recognition of turning intent. We conducted a driving simulator study while simultaneously measuring brain activation using high-density fNIRS. A neural network model for turning intent recognition was trained on the fNIRS and contextual data. The input variables were analyzed using SHAP (SHapley Additive exPlanations) feature importance analysis to show the positive effect of the inclusion of brain activation data. Both the model's evaluation and the feature importance analysis suggest that the combination of context information and brain activation leads to an improved turning intent recognition. The fNIRS results showed increased brain activation differences during the "turn" decision-making phase before turning execution in parts of the left motor cortices, such as the primary motor cortex (PMC; putative BA 4), premotor area (PMA; putative BA 6), and supplementary motor area (SMA; putative BA 8). Furthermore, we also observed increased activation differences in the left prefrontal areas, potentially in the left middle frontal gyrus (putative BA 9), which has been associated with the control of executive functions, such as decision-making and action planning. We hypothesize that brain activation measurements could be a more direct indicator with potentially high specificity for the turning behavior and thus help to increase the recognition model's performance.

Investigating Differences in Behavior and Brain in Human-Human and Human-Autonomous Vehicle Interactions in Time-Critical Situations

Some studies provide evidence that humans could actively exploit the alleged technological advantages of autonomous vehicles (AVs). This implies that humans may tend to interact differently with AVs as compared to human driven vehicles (HVs) with the knowledge that AVs are programmed to be risk-averse. Hence, it is important to investigate how humans interact with AVs in complex traffic situations. Here, we investigated whether participants would value interactions with AVs differently compared to HVs, and if these differences can be characterized on the behavioral and brain-level. We presented participants with a cover story while recording whole-head brain activity using fNIRS that they were driving under time pressure through urban traffic in the presence of other HVs and AVs. Moreover, the AVs were programmed defensively to avoid collisions and had faster braking reaction times than HVs. Participants would receive a monetary reward if they managed to finish the driving block within a given time-limit without risky driving maneuvers. During the drive, participants were repeatedly confronted with left-lane turning situations at unsignalized intersections. They had to stop and find a gap to turn in front of an oncoming stream of vehicles consisting of HVs and AVs. While the behavioral results did not show any significant difference between the safety margin used during the turning maneuvers with respect to AVs or HVs, participants tended to be more certain in their decision-making process while turning in front of AVs as reflected by the smaller variance in the gap size acceptance as compared to HVs. Importantly, using a multivariate logistic regression approach, we were able to predict whether the participants decided to turn in front of HVs or AVs from whole-head fNIRS in the decision-making phase for every participant (mean accuracy = 67.2%, SD = 5%). Channel-wise univariate fNIRS analysis revealed increased brain activation differences for turning in front of AVs compared to HVs in brain areas that represent the valuation of actions taken during decision-making. The insights provided here may be useful for the development of control systems to assess interactions in future mixed traffic environments involving AVs and HVs.

Designing dynamic distributed cooperative Human-Machine Systems

The paper presents a new approach to the development of cooperative human-machine systems in the Transportation domain which is currently researched in the European project D3CoS. A necessary precondition for the acceptance of cooperative human-machine systems with shared control is the confidence and trust of the user into the system. D3CoS tackles this important issue by addressing the cooperative system as the object and the target of the system development process. This new perspective, along with corresponding innovative methods, techniques and tools, shall allow the identification of optimal task and authority sharing approaches supported by intuitive human-machine interaction and user interfaces at an early stage of system development. This will support powerful teamwork between humans and machines or between machines and machines that is transparent, intuitive and easy to understand. The paper describes the research dimensions for the development of the methods, techniques and tools as well as first results.

Long-term treatment experience in a subject with Dunnigan-type familial partial lipodystrophy: efficacy of rosiglitazone

Dunnigan-type familial partial lipodystrophy (FPLD) is caused by mutations in LMNA, the gene that encodes nuclear lamins A and C. FPLD is characterized by peripheral fat loss, excess central adiposity, insulin resistance, and hyperlipidaemia, which are difficult to treat. We present our 2 years' experience of treatment with rosiglitazone in a subject with FPLD. Insulin requirement decreased significantly from 240 IU/day to 76 IU/day (range 20-240 IU/day) and serum triglyceride concentration was lowered from 13.7 +/- 14.4 mmol/l to 4.5 +/- 4.3 mmol/l and remained stable. Mean HbA(1c) prior to rosiglitazone therapy was 9.4 +/- 1.32% and decreased to 7.4 +/- 0.6% during therapy with rosiglitazone. This case demonstrates the benefits of PPARgamma-agonists on glycaemic control and dyslipidaemia in a patient with FPLD. This in turn implies that PPARgamma may play a pathophysiological role in FPLD.