https://www.cell.com/trends/cognitive-sciences/pdf/S1364-6613(02)00011-6.pdf

Evaluating perception in driving simulation experiments.

Development of Cue Integration in Human Navigation

In this paper we present a tutorial introduction to two important senses for biological and robotic systems — inertial and visual perception. We discuss the fundamentals of these two sensing modalities from a biological and an engineering perspective. Digital camera chips and micro-machined accelerometers and gyroscopes are now commodities, and when combined with today's available computing can provide robust estimates of self-motion as well 3D scene structure, without external infrastructure. We discuss the complementarity of these sensors, describe some fundamental approaches to fusing their outputs and survey the field.

An Introduction to Inertial and Visual Sensing

The Brain's Sense of Movement. By Alain Berthoz (Translated by Giselle Weiss). Cambridge, Massachusetts: Harvard University Press; 2000, 352 pp. $22.80. Ever wonder how certain people catch or bat a baseball hurled at blurring speeds? If you have, find yourself in a group whose intended or accidental success maybe a machine that pitches and throws like a ballplayer. Once this group of researchers articulates an accurate set of principles behind movement, deft engineering, persistence, and luck may converge to emulate nature. Although Berthoz's The Brain's Sense ofMovement, does not offer a science-fiction glimpse of agile androids that populate Asimov's novels, it provides an organized and fascinating way of thinking about movement. Berthoz takes the reader on a whirlwind tour of cognitive neuroscience topics: perception, coherence, memory, prediction, and adaptation. By examining these topics and using choice examples, Berthoz builds a persuasive case supporting his thesis that the brain is an anticipation machine. Even before delving into the intricacies of each of these topics, Berthoz's claim seems reasonable in light of evolution. In fact, Berthoz explains how evolution and improved neural systems that guide movement influence and drive each other: \"The species that passed the test of natural selection are those that figured out how to save a few milliseconds in capturing prey and anticipating the actions of predators, those whose brains were able to simulate the elements of the environment and choose the best way home, those able to memorize great quantities of information from past experience and use them in the heat of action.\" This cat and mouse games has honed the brain to take advantage of its parallel architecture, bypassing computing each trajectory in a Newtonian sense, and arriving at a solution by using heuristics developed over evolution. Heuristics play an important role in examples where a target exceeds physical limits of detection. For example, a baseball may move too quickly for the fovea to focus, however, the brain, and skeletal-muscular system use computational shortcuts to simulate, predict, adapt, and control the body in response to a changing environment. The first choice example that Berthoz highlights as a key computational shortcut is the derivative. Signals from receptors enable anticipation of future position of the head owing to their sensitivity to derivatives such as jerk, acceleration, and velocity. Another mathematical concept that Berthoz explores as a predictive tool is tensors. From what I learned, a tensor is a group of mathematical operators called matrices that carry out transformations among vectors. Between derivatives and vectors, Berthoz devotes several chapters to explaining how otoliths and semicircular canals use derivatives for linear and angular accelerations to predict while tensors receive perfunctory treatment. A balance between these two topics may better satisfy some readers. Certainly, derivatives and tensors alone cannot account for movement. Just as a calculator or computer derives its usefulness in a network, mathematical shortcuts for movement need to occur in the context of a circuit. Reading Berthoz's

The Brain's Sense of Movement.

Objective:The aim of this study was to outline a conceptual framework for understanding driving style and, on this basis, review the state-of-the-art research on driving styles in relation to road safety.Background:Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking.Method:A systematic literature search was conducted, including empirical, theoretical, and methodological research, on driving styles related to road safety.Results:A conceptual framework was proposed whereby driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorizing and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and ind...

/pdf/a-review-of-research-on-driving-styles-and-road-safety-5gd4ww77us.pdf

A Review of Research on Driving Styles and Road Safety

Experimental studies show that automobile drivers adjust their speed in curves so that maximum vehicle lateral accelerations decrease at high speeds. This pattern of lateral accelerations is described by a new driver model, assuming drivers control a variable safety margin of perceived lateral acceleration according to their anticipated steering deviations. Compared with a minimum time-to-lane-crossing (H. Godthelp, 1986) speed modulation strategy, this model, based on nonvisual cues, predicts that extreme values of lateral acceleration in curves decrease quadratically with speed, in accordance with experimental data obtained in a vehicle driven on a test track and in a motion-based driving simulator. Variations of model parameters can characterize "normal" or "fast" driving styles on the test track. On the simulator, it was found that the upper limits of lateral acceleration decreased less steeply when the motion cuing system was deactivated, although drivers maintained a consistent driving style. This is interpreted per the model as an underestimation of curvilinear speed due to the lack of inertial stimuli. Actual or potential applications of this research include a method to assess driving simulators as well as to identify driving styles for on-board driver aid systems.

Role of Lateral Acceleration in Curve Driving: Driver Model and Experiments on a Real Vehicle and a Driving Simulator

Motion cueing in a driving simulator is necessary for advanced studies requiring drivers to accurately perceive and control the motion of their vehicle. The Renault Dynamic Simulator uses a 6-axes electro-mechanical mobile platform with an adequate motion control software. The physical and perceptual validity of the motion cueing is analyzed with respect to actual vehicle acceleration data and human self-motion per-ception criteria. Within the actuator displacement limits, it is capable of directly rendering transient vehicle accelerations whereas sustained linear acceleration cues are simulated by a coordinated platform tilt. Accel-eration transients are extracted by high-pass filtering, but a classical implementation based on linear filters may produce artifacts in some key driving situations. A non-linear motion cueing algorithm was developed to anticipate and reduce these false motion cues.

Motion cueing in the renault driving simulator

Model-based predictive motion cueing strategy for vehicle driving simulators

In the framework of the European CARDS Project (Eureka n°1924) several experiments were carried out on the Renault Dynamic Simulator. The objective was to assess the contribution of kinesthetic cues provided by the motion platform when executing elementary driving tasks: braking and cornering at intersections. In the braking experiment, 11 standard drivers were asked to repeat 16 times the following sequence: reach a target velocity (80 km/h), maintain this velocity constant and decelerate in order to stop as close as possible to a specific signpost. Motion platform was activated for eight successive trials and inactivated for the remaining eight trials (or reverse). Results show that motion restitution prevents subjects from reaching too high and unrealistic decelerations, which are observed in a static simulator. Moreover, an adaptation of braking behavior appears in static mode, as drivers apply increasing braking force trial after trial, whereas braking strategy remained stable in the presence of motion cues. This result is discussed in particular by analysing the first seconds of braking behaviour and respective platform motion. In the cornering experiment, drivers took 2 laps around a square city block in a simulated urban environment free of traffic, in each of four platform conditions. In these conditions, lateral cues (sway+roll alone), longitudinal cues (surge+pitch alone), both of them or none were restituted, respectively. A global effect of the presence of the motion cueing shows on the trajectory pattern of the driven vehicle inside each 90° curve. These experiments are discussed in the context of sensorimotor integration while driving. Motion cues -when present even with limited amplitude- may be integrated in the driver control loop as additional inputs, providing they are relevant to the driving task considered.

Sensorimotor integration in a driving simulator: contributions of motion cueing in elementary driving tasks

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on...

Gilles Reymond

Papers

Role of Lateral Acceleration in Curve Driving: Driver Model and Experiments on a Real Vehicle and a Driving Simulator

Motion cueing in the renault driving simulator

Model-based predictive motion cueing strategy for vehicle driving simulators

Sensorimotor integration in a driving simulator: contributions of motion cueing in elementary driving tasks

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis