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This paper focuses on the collaboration between human drivers and intelligent vehicles. We propose a collaboration mechanism grounded on the concept of distributed cognition. With distributed cognition, intelligence does not lie just in the single entity but also in the interaction with the other cognitive components in a system. We apply this idea to vehicle intelligence, proposing a system distributed into two cognitive entities—the human and the autonomous agent—that together contribute to drive the vehicle. This account of vehicle intelligence differs from the mainstream research effort on highly autonomous cars. The proposed mechanism follows one of the paradigm derived from distributed cognition, the rider-horse metaphor: just like the rider communicates their intention to the horse through the reins, the human influences the agent using the pedals and the steering wheel. We use a driving simulator to demonstrate the collaboration in action, showing how the human can communicate and interact with the agent in various ways with safe outcomes.

This paper proposes a bio-inspired action selection mechanism, the multi-hypothesis sequential probability ratio test (MSPRT), as a decision making tool in the field of autonomous driving. We investigate the capability of the MSPRT algorithm to effectively select the optimal action whenever the autonomous agent is required to drive the vehicle. We present numerical simulations to demonstrate the robustness of the MSPRT action selection when dealing with noisy measurements., https://youtu.be/iU2NYahYo8I https://www.youtube.com/watch?v=uO14Id-Zjdc

This paper proposes a neural network model for visual perception in the context of autonomous driving inspired by the human cognition. Despite the growing research aimed at implementing self-driving cars, no artificial system can claim to have reached the driving performance of humans, yet. We believe that the theories about the human mind and its neural organization may reveal precious insights on how to design a more refined perceptual system for driving automation., https://youtu.be/JkenaxYJ9YU

In this work an artificial driving agent able to adapt its behavior depending on the specific situation and to generate human-like maneuvers was developed. The agent, called co-driver, was designed with a bio-inspired architecture, with an approach that takes advantage of ideas from cognitive science. In fact the decision process is based on the affordance competition hypothesis: the agent generates a representation of all possible actions given what is perceived in the environment and then chooses the optimal maneuver after inhibiting all the dangerous ones. In the first stage of the development process, atomic actions, called motor primitives, were identified. Then the co-driver was implemented with a layered architecture, where these motor primitives were combined in the upper levels in order to obtain more complex behaviors. Thanks to this particular architecture, the development process of the final artificial driver was split in two parts: initially an advanced driver assistance system (ADAS) was implemented and tested, and finally the architecture was extended in order to have an artificial driving agent for automated driving. In the first case the system was successfully tested on public roads and was able to warn the driver in case of dangerous scenarios, such as blind intersections. In the latter case, the co-driver could manage several challenging test cases in urban scenarios, from car following to cut-in scenario and curves. Finally, it was compared with a state-of-art driver model of CarMaker simulation software used by the main car manufacturers. Due to the achieved results, this work can be considered a valid potential solution for automated driving. In fact, the artificial driver was used as starting point for a European project (“Dreams4Cars”) that is currently in progress.

In this work we propose a number of relevant engineering applications that exhibit both a continuous and a discrete evolution, and are therefore suitably described by a recent formalism for hybrid dynamical systems. More specifically, (i) we design observer schemes for a nonsmooth disturbance arising in AC/DC conversion, which we then cancel from a desirable signal; (ii) we show how reset actuation applied to nonlinear mechanical systems can at the same time sustain or damp oscillations; (iii) we study the feedback interconnection of a classical proportional-integral-derivative controller with a sliding mass under Coulomb friction through differential inclusions. In the context of dynamical systems, we analyze the properties of these applications in terms of asymptotic stability through Lyapunov functions tailored for hybrid systems. Instead of the standard Lyapunov conditions, we prove asymptotic stability through weaker, or relaxed, conditions that are compensated by additional (structural) properties that may be easier to verify.

The introduction of the Brake By Wire is replacing the traditional mechanical control systems with ECUs and it is raising the need to reproduce feelings of eliminated static mechanical components (i.e. hydraulic fluids, pumps and cylinders). Thanks to electromechanical actuators and human-machine interfaces (i.e. active pedal) it is possible to reproduce such feelings and, therefore, arbitrarily change their features. In this way it will be possible to customize the pedal feelings and the vehicle deceleration needed depending on several factors (i.e. surrounding braking scenario, driver characteristics, race vs day-by-day driving condition). Since braking maneuvers are typically critical and involve the driver, the design and development of brake by wire system must start from the consideration of human factors in order to increase acceptance and braking effectiveness. The objective of this research was to redesign the pedal feelings, making them adaptable to the surrounding. Driver acceptance and braking effectiveness could be highly improved by means of adaptive pedal feelings. The starting points of this research were humans factors in the braking domain. Literature and relevant studies have been taken into consideration to put into evidence human mechanisms and behaviors during braking phases. On such basis, two main results have been found out: braking use cases and pedal feeling curves. With regard to the pedal feelings curves, 4 different pedal curves which describe both force on brake pedal travel and acceleration on brake pedal travel are designed. The pedal feeling depends on several factors like the pedal travel, the pedal idle travel, the effort, responsiveness, deceleration perceived, ease of balance (i.e. ease of modulation), gradual braking and so on. Regarding braking use cases, they are described by vehicle data as speed, acceleration, angles and relevant rates, engine rpm, gas and brake pedal position/speed and so on. These use cases have been clustered in order to meet the 4 pedal curves. The research continued on the implementation of a Matlab/Simulink/Stateflow model for the use case recognition. Basing on the vehicle data, the model is able to find out in which use case the vehicle is (parking, low speed maneuvers, emergency, downhill, and so on). Once it finds out the scenario, the model applies the most appropriate pedal feeling curve (both force feedback and deceleration needed). In the end, the model commands an EC brushless motor which is responsible of the changing of static springs force feedback of the pedal. The scenario recognition model has been validated through vehicle data on real road whereas the pedal feeling and relevant motor behaviors have been validate on bench tests.