On the human control of vehicles: an experimental study of acceleration

Eur. Transp. Res. Rev. (2014) 6:157–170 DOI 10.1007/s12544-013-0120-2 ORIGINAL PAPER On the human control of vehicles: an experimental study of acceleration Paolo Bosetti & Mauro Da Lio & Andrea Saroldi Received: 7 June 2013 / Accepted: 17 September 2013 / Published online: 28 September 2013 # The Author(s) 2013. This article is published with open access at SpringerLink.com Abstract This paper presents an experimental investigation of human control of vehicles carried out on the basis of general theories on human movement. The longitudinal and lateral accelerations are studied, and their relations with theories of motor optimality principles, such as minimum jerk, minimum variance, and the two-thirds power law are highlighted. Data have been collected during the final experimental phase of the EU interactIVe project, in which a vehicle developed by Centro Ricerche Fiat has been used to demonstrate driver continuous support produced by an artificial codriver, within a shared initiative framework. 24 subjects drove the vehicle on a test route twice: once with the system active, the other with the system silent. The test route is composed of urban arterials, extra urban and motorway roads, and takes approximately 40–45 min to be driven. The total database thus amounts to ~35 h of driving data recordings, for a total of ~1.2 M samples per signal. Statistical summary data are presented, which describe human preferred accelerations, correlation between acceleration, curvature, and speed, and between longitudinal and lateral acceleration. Different driving modalities, corresponding to different motor strategies and primitives, are revealed. Comparisons with literature data are also made and discussed. The summary statistics may be useful for the design of future ADAS systems, and indeed they have been collected for the final tuning of the interactIVe co-driver. P. Bosetti (*) : M. Da Lio Department of Industrial Engineering, University of Trento, Trento, Italy e-mail: M. Da Lio e-mail: A. Saroldi Centro Ricerche Fiat, 10043 Orbassano, Italy e-mail: Keywords Driver modeling . Intelligent vehicles . Human machine interaction . Advanced driver assistance systems . Man–machine systems 1 Human sensory-motor strategies THE understanding of human movement plays a central role in many application domains. Recent theories say that the human brain motor system is active in several covert (non-executed) motor activities, such as motion planning and observation of other people movements (mirroring) [1–3]. It is believed that the ability to predict how a person would move — given an objective and in conjunction with the observation of other people actual movements — is at the origin of the understanding of intentions [4], empathy, and ultimately social interactions [5]. Such a framework has also been adopted for humanrobot interactions [5, 6]. Within the EU interactIVe project [7] the Authors adopted the same conceptual framework for developing an artificial cognitive system (named co-driver) able to understand the driver intentions and to produce a variety of Driver Assistance Functions [8–10]. Several authors showed that general human sensory-motor strategies are learnt and optimized [11–15]. Human movements — as for example the task of reaching an object — are typically carried out as optimized motor units [16, 17], which are sent to execution in a feed-forward fashion, and while still in execution, they may be updated. Updating corrects only task-relevant deviations (i.e. the goal is pursued from the deviated position, without returning to the previous planned trajectory), which is known as minimum intervention principle [12]. The optimization criterion is most often said to be minimum jerk, and in facts human movements are smooth. However, further studies have shown that minimum jerk may be a byproduct of another optimality criterion, which is minimum 158 variance [15, 18]. According to this criterion, humans learn how to control movements so that the effect of motor neuron noise is minimized, and thus they achieve the best tradeoff between accuracy and speed. Human movement is also known to withstand a velocitycurvature-acceleration constraint known as the two-thirds power law, which states that while hand-tracing an arc, the angular velocity is limited by the two thirds power of the local curvature [19, 20]. As for the jerk, this is believed to be a byproduct of the same minimum variance principle [15, 18, 21]. The problem of producing a body movement — such as getting a hand to a desired target — subject to the minimum variance principle is an optimal control problem. The simulation theory of cognition [1] says that humans learn forward and inverse models of the plants they are going to control [22, 23] (starting with, but not limited to, their own body), so that sensorial consequences of actions may be predicted, and actions that achieve desired perceptual goals may be produced [1, 3]. In this conceptual framework, the control of vehicles may be seen as a particular case of plant control (in control theory, “plant” means the dynamical system to be controlled), achieved by learning forward and inverse models of the vehicle dynamics. This justifies the current opinion in vehicle dynamics that drivers have mental models of the vehicles, used to anticipate the effect of control [24, 25]. Optimality principles that lead to efficient control of the human body may be reasonably postulated for the control of vehicles too. It is thus no surprise that optimal control and model predictive control approaches have been successfully used to model drivers [24, 25], including those presented in Authors’ previous works [8, 26–30]. Moreover, road bends are driven with a limiting lateral acceleration that decreases with curvature [28, 31–38], which is analogous of the two-thirds power law. Some authors explained the speed-curvature correlation as a way of minimizing the effects of steering errors [32, 36], which is the same conceptual argument of minimum variance criterion used to explain the origin of the two-thirds power law. There is consequently a theoretical justification for looking at the curvature-acceleration-speed relationships as just another facet of more general human motor optimality criteria and put it in relation with the two-thirds power law. As for what concerns the longitudinal control, the accelerations used in human driving may also be found in some previous studies of Adaptive Criuse Control (ACC) systems [39, 40], while correlations between longitudinal and lateral acceleration are pointed out in other papers [28, 41–43]. The data collected and presented in the present work will be compared to this literature references in the following sections. As a final theoretical consideration, it is worth recalling the hierarchical nature of human behaviors, of which driving is one case. The recently proposed Extended Control Model (ECOM) [44, 45] explains the driving act (...truncated)