For a decade, researchers have focused on the development and deployment of road automated mobility. In the development of autonomous driving embedded systems, several stages are required. The first one deals with the perception layers. The second one is dedicated to the risk assessment, the decision and strategy layers and the optimal trajectory planning. The last stage addresses the vehicle control/command. This paper proposes an efficient solution to the second stage and improves a virtual Cooperative Pilot (Co-Pilot) already proposed in 2012. This paper thus introduces a trajectory planning algorithm for automated vehicles (AV), specifically designed for emergency situations and based on the Autonomous Decision-Support Framework (ADSF) of the EU project Trustonomy. This algorithm is an extended version of Elastic Band (EB) with no fixed final position. A set of trajectory nodes is iteratively deduced from obstacles and constraints, thus providing flexibility, fast computation, and physical realism. After introducing the project framework for risk management and the general concept of ADSF, the emergency algorithm is presented and tested under Matlab software. Finally, the Decision-Support framework is implemented under RTMaps software and demonstrated within Pro-SiVIC, a realistic 3D simulation environment. Both the previous virtual Co-Pilot and the new emergency algorithm are combined and used in a near-accident situation and shown in different risky scenarios.

As for the level of tail-water often rose when the situation of tailrace river changing during the process of construction of hydroelectric power station, one-dimensional mathematical model is used through two procedures to solve this problem. In the first, the sensitivity of three key parameters including roughness, cross-section spacing Δs, starting regulation level and section in the mathematical model were discussed. The results indicate that: (1) Positive relationship between ΔY/Y and n, shows the greater flow rate, the more sensitive of water depth changes to roughness coefficient; (2) The cross-section spacing Δs affect the large flow more obvious than the smaller; (3) The effect of the middle and lower reaches of river are bigger than the upstream. Research results provided guidance of model parameters selection.