Road users rarely use explicit communication when interacting in today’s traffic: Implications for Automated Vehicles

To be successful, automated vehicles (AVs) need to be able to manoeuvre in mixed traffic in a way that will be accepted by road users, and maximises traffic safety and efficiency. A likely prerequisite for this success is for AVs to be able to communicate effectively with other road users in a complex traffic environment. The current study, conducted as part of the European project interACT, investigates the communication strategies used by drivers and pedestrians while crossing the road at six observed locations, across three European countries. In total, 701 road user interactions were observed and annotated, using an observation protocol developed for this purpose. The observation protocols identified 20 event categories, observed from the approaching vehicles/drivers and pedestrians. These included information about movement, looking behaviour, hand gestures, and signals used, as well as some demographic data. These observations illustrated that explicit communication techniques, such as honking, flashing headlights by drivers, or hand gestures by drivers and pedestrians, rarely occurred. This observation was consistent across sites. In addition, a follow-on questionnaire, administered to a sub-set of the observed pedestrians after crossing the road, found that when contemplating a crossing, pedestrians were more likely to use vehicle-based behaviour, rather than communication cues from the driver. Overall, the findings suggest that vehicle-based movement information such as yielding cues are more likely to be used by pedestrians while crossing the road, compared to explicit communication cues from drivers, although some cultural differences were observed. The implications of these findings are discussed with respect to design of suitable external interfaces and communication of intent by future automated vehicles

Optimization of Muscle Activity for Task-Level Goals Predicts Complex Changes in Limb Forces across Biomechanical Contexts

Optimality principles have been proposed as a general framework for understanding motor control in animals and humans largely based on their ability to predict general features movement in idealized motor tasks. However, generalizing these concepts past proof-of-principle to understand the neuromechanical transformation from task-level control to detailed execution-level muscle activity and forces during behaviorally-relevant motor tasks has proved difficult. In an unrestrained balance task in cats, we demonstrate that achieving task-level constraints center of mass forces and moments while minimizing control effort predicts detailed patterns of muscle activity and ground reaction forces in an anatomically-realistic musculoskeletal model. Whereas optimization is typically used to resolve redundancy at a single level of the motor hierarchy, we simultaneously resolved redundancy across both muscles and limbs and directly compared predictions to experimental measures across multiple perturbation directions that elicit different intra- and interlimb coordination patterns. Further, although some candidate task-level variables and cost functions generated indistinguishable predictions in a single biomechanical context, we identified a common optimization framework that could predict up to 48 experimental conditions per animal (n = 3) across both perturbation directions and different biomechanical contexts created by altering animals' postural configuration. Predictions were further improved by imposing experimentally-derived muscle synergy constraints, suggesting additional task variables or costs that may be relevant to the neural control of balance. These results suggested that reduced-dimension neural control mechanisms such as muscle synergies can achieve similar kinetics to the optimal solution, but with increased control effort (≈2×) compared to individual muscle control. Our results are consistent with the idea that hierarchical, task-level neural control mechanisms previously associated with voluntary tasks may also be used in automatic brainstem-mediated pathways for balance

Postsynaptic expression of long-term potentiation in the rat dentate gyrus demonstrated by variance-mean analysis

Long-term potentiation (LTP) of synaptic transmission is the putative mechanism underlying learning and memory. Despite intensive study, it remains controversial whether LTP is expressed at a pre- or postsynaptic locus. A new approach was used to investigate this question at excitatory synapses from the medial perforant path (MPP) onto granule cells in the hippocampal dentate gyrus. The variance of the evoked synaptic amplitude was plotted against mean synaptic amplitude at several different Cd2+ concentrations. The slope of the variance-mean plot estimates the average amplitude of the response following the release of a single vesicle of transmitter (Qav). A presynaptic modulation should not affect Qav, but a postsynaptic modulation should alter it.The variance-mean technique was tested by applying the analysis before and after three different synaptic modulations: (i) a reduction in Qav by the addition of the competitive antagonist CNQX; (ii) a reduction in the average probability of transmitter release (Pav) by the addition of baclofen; and (iii) an increase in the number of active synaptic terminals (N) by increasing the stimulus strength. CNQX reduced the average synaptic amplitude and Qav to the same extent, consistent with a postsynaptic action. In contrast, neither a change in N nor Pav altered Qav. This confirms that the variance-mean technique can distinguish between a pre- and a postsynaptic site of modulation.Induction of LTP increased EPSC amplitude by 50 ± 0·4% (n = 5) and, in the same cells, increased Qav by 47 ± 0·6%. There was no significant difference between the increase in EPSC amplitude and the increase in Qav. Thus, LTP of the MPP input to dentate granule cells can be explained by an increase in the postsynaptic response to transmitter

Recruitment by size and principle of least action

The size principle implies that the motoneurons of a muscle pool are activated in ascending order of their sizes when that pool of motoneurons receives a common, increasing input. We suggest a simple discrete Lagrangian for an isometrically contracting skeletal muscle. Minimizing the time integral of this Lagrangian leads to recruitment of motor units according to increasing size