Publications

A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians

Abstract

Driving requires the ability to handle unpredictable situations. Since it is not always possible to predict an impending danger, a good driver should preventively assess whether a situation has risks and adopt a safe behavior. Considering, in particular, the possibility of a pedestrian suddenly crossing the road, a prudent driver should limit the traveling speed. We present a work exploiting reinforcement learning to learn a function that specifies the safe speed limit for a given artificial driver agent.

READ MORE

The Biasing of Action Selection Produces Emergent Human-Robot Interactions in Autonomous Driving

Abstract

This letter describes a means to produce emergent collaboration between a human driver and an artificial co-driver agent. The work exploits the hypothesis that human-human cooperation emerges from a shared understanding of the given context’s affordances and emulates the same principle: the observation of one agent’s behavior steers another agent’s decision-making by favoring the selection of the goals that would produce the observed activity.

READ MORE

Occupancy Grid Mapping with Cognitive Plausibility for Autonomous Driving Applications

Abstract

This work investigates the validity of an occupancy grid mapping inspired by human cognition and the way humans visually perceive the environment. This query is motivated by the fact that, to date, no autonomous driving system reaches the performance of an ordinary human driver. The mechanisms behind human perception could provide cues on how to improve common techniques employed in autonomous navigation—specifically the use of occupancy grids to represent the environment.

READ MORE

Neurocognitive–Inspired Approach for Visual Perception in Autonomous Driving

Abstract

Since the last decades, deep neural models have been pushing forward the frontiers of artificial intelligence. Applications that in the recent past were considered no more than utopian dreams, now appear to be feasible. The best example is autonomous driving. Despite the growing research aimed at implementing autonomous driving, no artificial intelligence can claim to have reached or closely approached the driving performance of humans, yet.

READ MORE

Modelling longitudinal vehicle dynamics with neural networks

Abstract

This paper studies neural network models of vehicle dynamics. We consider both models with a generic layer architecture and models with specialised topologies that hard-wire physics principles. Network pre-wiring is limited to universal laws; hence it does not limit the network modelling abilities on one side but allows more robust and interpretable models on the other side. Four different network types (with and without pre-wired structure, recursive and non-recursive) are compared for the longitudinal dynamics of a car with gears and two controls (brake and engine).

READ MORE

On Reliable Neural Network Sensorimotor Control in Autonomous Vehicles

Abstract

This paper deals with (deep) neural network implementations of sensorimotor control for automated driving. We show how to construct complex behaviors by re-using elementary neural network building blocks that can be trained and tested extensively; one of our goals is to mitigate the “black box” and verifiability issues that affect end-to-end trained networks.

READ MORE

A mental simulation approach for learning neural-network predictive control (in self-driving cars)

Abstract

This paper presents a novel approach to learning predictive motor control via “mental simulations”. The method, inspired by learning via mental imagery in natural Cognition, develops in two phases: first, the learning of predictive models based on data recorded in the interaction with the environment; then, at a deferred time, the synthesis of inverse models via offline episodic simulations. Parallelism with human-engineered control-theoretic workflow (mathematical modeling the direct dynamics followed by optimal control inversion) is established.

READ MORE

On the Road With 16 Neurons: Towards Interpretable and Manipulable Latent Representations for Visual Predictions in Driving Scenarios

Abstract

This paper proposes a strategy for visual perception in the context of autonomous driving. Humans, when not distracted or drunk, are still the best drivers you can currently find. For this reason, we take inspiration from two theoretical ideas about the human mind and its neural organization. The first idea concerns how the brain uses structures of neuron ensembles that expand and compress information to extract abstract concepts from visual experience and code them into compact representations.

READ MORE

Agent Architecture for Adaptive Behaviors in Autonomous Driving

Abstract

Evolution has endowed animals with outstanding adaptive behaviours which are grounded in the organization of their sensorimotor system. This paper uses inspiration from these principles of organization in the design of an artificial agent for autonomous driving. After distilling the relevant principles from biology, their functional role in the implementation of an artificial system are explained.

READ MORE

Longitudinal Vehicle Dynamics: A Comparison of Physical and Data-Driven Models Under Large-Scale Real-World Driving Conditions

Abstract

Mathematical models of vehicle dynamics will form essential components of future autonomous vehicles. They may be used within inverse or forward control loops, or within predictive learning systems. Often, nonlinear physical models are used in this context, which, though conceptually simple (especially for decoupled, longitudinal dynamics), may be computationally costly to parameterise and also inaccurate if they omit vehicle-specific dynamics.

READ MORE



This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 731593.