Periodic Reporting for period 2 - AutoMate (Automation as accepted and trustful teamMate to enhance traffic safety and efficiency)
Periodo di rendicontazione: 2018-03-01 al 2019-08-31
The success of future more complex and more automated vehicles will depend on how well they interact, communicate and cooperate with humans both inside and outside the vehicle. The top-level objective of AUTOMATE is to develop, evaluate and demonstrate the “TeamMate Car” concept as a major enabler of highly automated vehicles. In this context, driver and automation are regarded as members of one team that understand and support each other in pursuing cooperatively the goal of driving safely, efficiently and comfortably from A to B. Enhanced safety can be achieved, by using the strength of both the automation and human driver in a dynamic way."
1. Sensor and Communication Platform
2. Probabilistic Driver Modelling and Learning
3. Probabilistic Vehicle and Situation Modelling
4. Adaptive Driving Manoeuvre Planning, Execution and Learning
5. Online Risk Assessment
6. TeamMate HMI
7. TeamMate System Architecture.
The corresponding innovations are integrated and implemented on several car simulators and real vehicles to evaluate and demonstrate the project progress and results in real-life traffic conditions.
In particular, the main work, performed so far in AUTOMATE project, is related to the first development and validation of the aforementioned enablers, including the general system architecture and the specific one for each demonstrator. Moreover, the integration of components and modules in these demos has been already started. More details are available in the technical report document, where we explain the work done, WP by WP and Task by Task.
In the second part of the project we expect to refine the enablers based on the previous validation phase (activity already started) and then, to implement and integrate the corresponding modules into the project demonstrators. Finally, we will carry out the evaluation phase, including both the technical (objective) tests and the subjective tests (with real users).
Bayesian probabilistic formalism is also used to gather information on individual objects into one coherent model, by placing objects into relation with each other and to infer additional plausible information about objects based on the recognized scene. We have innovated object-tracking algorithms to be able to handle Multi-Object-Tracking, taking into account state uncertainties, existence uncertainties and contradicting information in a single algorithm.
In the past years much work was done in the field of driving maneuver planning and execution for automated vehicles. We have reused such automation functions, but upgraded them with new driver adaptive functionality. In particular, on one hand, the planning algorithms can incorporate and involve the driver in the driving task to generate a dynamically type of responsibility assignment. On the other hand, the system is able to learn from the driving style of the human driver in a wide range of situations. Thus, the system anticipates and incorporates inter- and intra-individual differences in driving style and driver decisions.
Another crucial challenge is to assess a huge number of possible evolutions of the traffic scene in real time. Current approaches to risk assessment aim at checking whether any given trajectory is feasible or will likely lead to a collision. However, these approaches only consider the safety of individual actions and only address the fully automated case. In order to consider dynamic task distributions between driver and automation in AUTOMATE, we have implemented situation-dependent corridors of safe actions.
The last innovation is about the Human-Machine Interaction (HMI) aspect, whose main goal is to keep the driver sufficiently in the loop or to get her/him back in the control-loop, according to her/his actual state and driving tasks. Currently, there has not been any research yet on finding the most comprehensive way to convey the rationale for autonomous actions to drivers (some studies exist on applying the Ecological Interface Design (EID) approach for communicating automation behavior, but this has only been achieved for isolated automation functions). AUTOMATE used EID in a completely new way to integrate all relevant information on the traffic, driver and automation, by showing safe driving corridors and constraints on these corridors using graphical means. Therefore, we are creating a Navigation-Centred Driving Cluster (NCDC), that significantly improves the initial concept by integrating all TeamMate relevant information (e.g. driver and automation state to intuitively show imminent risks, as well as distant hot spots where the vehicle may request the support of the driver). Moreover, we will research Personalised Multi-model Communication Preferences, in form of “concurred abbreviations”. The intention is to communicate “Why” information in a personalized way, since this can improve trust and acceptance in the automation, supporting the driver in the decision-making process, via different HMI channels.