## 5 Discussion and Conclusions  
This paper introduced the Autonomous Decision-Support Framework, developed by Eiffel University for the EU project Trustonomy, and focused on the problem of trajectory planning in emergency situations. Some results of the virtual Co-Pilot were given in a simulation environment (Pro-SiVIC and RTMaps) and embedded in a real prototype. A complementary algorithm specifically designed for emergencies has been proposed and developed in order to propose unusual maneuvers allowing taking into account critical situations. At this moment, this \"emergency\" system has been developed and tested in simulations, using Matlab and Pro-SiVIC. Its main characteristics are the following. * It is based on EB's trajectory generation, for fast computation. * Unlike in classical methods, the last point of the trajectory is not fixed, but simply attracted to the general direction of motion, to give more flexibility in different emergency situations. * The current orientation and speed of the vehicle are taken into account using additional ad hoc forces during the trajectory generation, to ensure mechanical feasibility even when obstacles are very close. This algorithm was used in the Trustonomy framework for risk management, where the decision-support module can switch between the normal Autonomous mode and this new Emergency mode depending on the risk level. Simulation results in a realistic 3D environment showcased the benefits of this hybrid approach in near-accident situations. However, the simulations presented here have several limitations which will have to be tackled in future works. First, this model has many free parameters, so the calibration step to balance the different forces requires a special attention, to ensure the highest level of safety in any situation. The behavior of the vehicle when the collision is unavoidable (or when the trajectory given returned the algorithm is not feasible) is not clearly defined at the moment. The prediction of the movement of obstacles in dynamic settings is very basic at the moment and could be improved. Finally, interactions and driving sharing with the human driver of the ego-vehicle was not taken into account in the emergency part, although it has been used with [PERSON]'s model before, this paper was only concerned with how the vehicle would react in an emergency situation, and not the pair vehicle and driver. As the Trustonomy project continues in 2021, the next steps of developing an autonomous Decision-Support framework are the following. * Calibration and testing of the Emergency trajectory planning algorithm will be continued in different scenarios (urban, scenarios with multiple vehicles, pedestrians, etc). * The current algorithms will be included in the general ADSF framework, which includes a Driver State Monitoring system and a Driver Intervention Performance Assessment (DIPA) module to monitor mode transitions and driver-related risk management at all times. * The general framework will be tested on a new real vehicle in 2021 in France, possibly using virtual obstacles generated from Pro-SiVIC platform (Vehicle in the Loop architecture with communication means). * Different perception architectures and perception data will be used in order to assess the capability of the full ADS in adverse and degraded conditions involving possible sensor failures. **Author Contributions:** conceptualization, O.O. and D.G. ; methodology, O.O. ; software, W.X. ; validation, W.X. and R.S. ; formal analysis, R.S. ; writing--original draft preparation, W.X. ; writing--review and editing, D.G. ; visualization, W.X. ; supervision, D.G. and R.S. ; project administration, O.O. All authors have read and agreed to the published version of the manuscript. **Funding:** This paper is supported by European Union's Horizon 2020 research and innovation program under grant agreement No. 815003, project Trustonomy (Building Acceptance and Trust in Autonomous Mobility). **Data Availability Statement:** Data sharing not applicable.