Will intelligent vehicles evolve into human-peer robots?

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Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems

This position paper introduces the concept of artificial “co-drivers” as an enabling technology for future intelligent transportation systems. In Sections I and II, the design principles of co-drivers are introduced and framed within general human–robot interactions. Several contributing theories and technologies are reviewed, specifically those relating to relevant cognitive architectures, human-like sensory-motor strategies, and the emulation theory of cognition. In Sections III and IV, we present the co-driver developed for the EU project interactIVe as an example instantiation of this notion, demonstrating how it conforms to the given guidelines. We also present substantive experimental results and clarify the limitations and performance of the current implementation. In Sections IV and V, we analyze the impact of the co-driver technology. In particular, we identify a range of application fields, showing how it constitutes a universal enabling technology for both smart vehicles and cooperative systems, and naturally sets out a program for future research

Saspence - Safe Speed And Safe Distance: Project Overview and Customer Benefit Analysis of a Novel Driver’s Collision Avoidance Support System

In Europe, a considerable amount of lives lost in traffic accidents is due to inappropriate vehiclespeed or inappropriate headway. Excessive speed is acknowledged as one of the major causes ofaccidents being responsible for about one-third of crashes, and contributing to the death of around1.200 people each year and more than 100.000 injuries. In addition, rear-end and chain accidents,which are mainly caused by inappropriate headways, altogether account for another 15% of all roadaccidents. In order to improve driving safety it is therefore of great importance to develop anintelligent system that helps the driver in reducing risky and dangerous situations related to theaforementioned factors. Such a system is the goal of the SAPENCE Project (part of IP PREVENT).The safety gains that are expected from systems capable to appropriately warn the driver in case ofexcessive speed and small headway look very promising

Biologically guided driver modeling: the stop behavior of human car drivers

This paper presents a principled approach to the modeling of human drivers--applied to stop behavior--by uniting recent ideas in cognitive science and optimal control. With respect to the former, we invoke the affordance competition hypothesis, according to which human behavior is produced by resolving the competition between action affordances that are simultaneously instantiated in response to the environment. From the theory of optimal control, we deploy motor primitives based on minimum jerk as the potential suite of actions. Furthermore, we invoke a layered control architecture, which carries out action priming and action selection sequentially, to model the biological affordance competition process. Motor output may be directed to distinct motor channels, which may be partially inhibited, e.g., to model gas pedal release saturation. Within this architecture, two types of motor units--''deceleration'' acting on a gas pedal channel and ''brake'' acting on a brake pedal channel--are sufficient to model, with remarkable accuracy, the various phases that can be observed in human maneuvers in stopping a car, namely: gas release, gas chocked, brake, and final brake release at stop. The model is validated using experimental data collected in 16 different stop locations, from roundabouts to traffic lights. We also carry out a comparison with the well-known Intelligent Driver Model, discuss the scaling of this framework to more general driving scenarios and finally give an example application where the driver model is used, within a mirroring process, to infer the human driver intentions