Human-robot interaction (HRI) requires quantitative models of proxemics and trust for robots to use in negotiating with people for space. Hall’s theory of proxemics has been used for decades to describe social interaction distances but has lacked detailed quantitative models and generative explanations to apply to these cases. In the limited case of autonomous vehicle interactions with pedestrians crossing a road, a recent model has explained the quantitative sizes of Hall’s distances to 4% error and their links to the concept of trust in human interactions. The present study extends this model by generalising several of its assumptions to cover further cases including human-human and human-robot interactions. It tightens the explanations of Hall zones from 4% to 1% error and fits several more recent empirical HRI results. This may help to further unify these disparate fields and quantify them to a level which enables real-world operational HRI applications.

Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behavior modelling, prediction and interaction control.

Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behavior, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behavior, high-level psychological and social modelling of pedestrian behavior still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV control.

Autonomous vehicle control is well understood for local- [15], good approximations exist such as particle ?ltering,ization, mapping and planning in un-reactive environ- which make use of large compute power to draw samplesments, but the human factors of complex interactions near solutions.stood [16], and despite its exact solution being NP-hardwith other road users are not yet developed.Route planning in non-interactive envi-ronments also has well known tractable solutions such asThis po-the A-star algorithm. Given a route, localizing and con-sition paper presents an initial model for negotiation be-trol to follow that route then becomes a similar task totween an autonomous vehicle and another vehicle at anthat performed by the 1959 General Motors Firebird-IIIunsigned intersections or (equivalently) with a pedestrianself-driving car [1], which used electromagnetic sensingat an unsigned road-crossing (jaywalking), using discreteto follow a wire built into the road.Such path follow-sequential game theory. The model is intended as a ba- ing, using wires or SLAM, can then be augmented withsic framework for more realistic and data-driven future simple safety logic to stop the vehicle if any obstacle isextensions. The model shows that when only vehicle po- in its way, as detected by any range sensor.sition is used to signal intent, the optimal behaviors for open source systems for this level of `self-driving' are nowboth agents must include a non-zero probability of al- widely available [6].lowing a collision to occur.In contrast,This suggests extensions toproblems that these vehicles will facearound interacting with other road users are much harderreduce this probability in future, such as other forms ofboth to formulate and solve. Autonomous vehicles do notsignaling and control. Unlike most Game Theory appli-just have to deal with inanimate objects, sensors, andcations in Economics, active vehicle control requires real-maps.time selection from multiple equilibria with no history,They have to deal with other agents, currentlyhuman drivers and pedestrians and eventually other au-and we present and argue for a novel solution concept,meta-strategy convergence , suited to this task.

A biomimetic mobile robot called “Shrewbot” has been built as part of a neuroethological study of the mammalian facial whisker sensory system. This platform has been used to further evaluate the problem space of whisker based tactile Simultaneous Localisation And Mapping (tSLAM). Shrewbot uses a biomorphic 3-dimensional array of active whiskers and a model of action selection based on tactile sensory attention to explore a circular walled arena sparsely populated with simple geometric shapes. Datasets taken during this exploration have been used to parameterise an approach to localisation and mapping based on probabilistic occupancy grids. We present the results of this work and conclude that simultaneous localisation and mapping is possible given only noisy odometry and tactile information from a 3-dimensional array of active biomimetic whiskers and no prior information of features in the environment.

Future robots may need to navigate where visual sensors fail. Touch sensors provide an alternative modality, largely unexplored in the context of robotic map building. We present the first results in grid based simultaneous localisation and mapping (SLAM) with biomimetic whisker sensors, and show how multi-whisker features coupled with priors about straight edges in the world can boost its performance. Our results are from a simple, small environment but are intended as a first baseline to measure future algorithms against.

Tactile sensing can be an important source of information for robots, and texture discrimination in particular is useful in object recognition and terrain identification. Whisker based tactile sensing has recently been shown to be a promising approach for mobile robots, using simple sensors and many classification approaches. However these approaches have often been tested in limited environments, and have not been compared against one another in a controlled way. A wide range of whisker-object contact poses are possible on a mobile robot, and the effect such contact variability has on sensing has not been properly investigated. We present a novel, carefully controlled study of simple surface texture classifiers on a large set of varied pose conditions that mimic those encountered by mobile robots. Namely, single brief whisker contacts with textured surfaces at a range of surface orientations and contact speeds. Results show that different classifiers are appropriate for different settings, with spectral template and feature based approaches performing best in surface texture, and contact speed estimation, respectively. The results may be used to inform selection of classifiers in tasks such as tactile SLAM.

We present a mapping of the hippocampal formation onto a Temporal Restricted Boltzmann Machine [1] based architecture, running a deterministic version of Gibbs sampling, and extended with a lostness detection and recovery circuit modelled on subiculum and septal acetylcholine (ACh). The mapping approximates Bayesian filtering, which infers both auto-associative de-noised percepts and temporal sequences, the latter including sequences of places during navigation. Inference may be viewed as a neurally implemented particle filter with a single particle-as suggested previously [2] as a purely behavioural animal model.

Many rodents use their whiskers to distinguish objects by surface texture. To examine possible mechanisms for this discrimination, data from an artificial whisker attached to a moving robot was used to test texture classification algorithms. This data was examined previously using a template-based classifier of the whisker vibration power spectrum. Motivated by a proposal about the neural computations underlying sensory decision making, we classified the raw whisker signal using the related `naive Bayes' method. The integration time window is important, with roughly 100ms of data required for good decisions and 500ms for the best decisions. For stereotyped motion, the classifier achieved hit rates of about 80% using a single (horizontal or vertical) stream of vibration data and 90% using both streams. Similar hit rates were achieved on natural data, apart from a single case in which the performance was only about 55%. Therefore this application of naive Bayes represents a biologically motivated algorithm that can perform well in a real-world robot task.