Safe Vehicle Trajectory Planning in an Autonomous Decision Support Framework for Emergency Situations

Abstract

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.

Generic Simulation Framework for Evaluation Process: Applied to AI-powered Visual Perception System in Autonomous Driving

“…In our experimental setup, we use a visual perception system based on a monocular camera. This system is implemented as a module within the RTMaps TM framework and is an extension of the driving system developed in the H2020 Trustonomy project [16]. The camera-based perception system is specifically designed to provide essential functionalities such as object identification, localization, and tracking.…”

Intelligent, In-Vehicle Autonomous Decision-Making Functionality for Driving Style Reconfigurations

“…The f (xi) values regarding the capabilities of the driving styles according to (2), are shown in Figure 10. The curves show that the acquisition of knowledge is more difficult in DS = 1 and 3 (aggressive and defensive driving styles), compared to the curve of DS = 2 (normal driving style), due to the significant delay in their increase.…”

“…Intelligent connected vehicles (ICVs) continue to attract immense research interest [1][2][3][4], as they constitute a transformative technology that holds a great promise in providing road safety, transport efficiency, driving comfort, and eco-friendly mobility.…”

An Emergency Escape Strategy based on Reasonable Dynamic Constraints for Autonomous Vehicles

“…4 Jingyun Xu is with the School of Transportation Science and Engineering, Beihang University, Beijing, China (e-mail: ZF2213124@buaa.edu.cn). 5 Jiayi Lu is with the School of Transportation Science and Engineering, Beihang University, Beijing, China (e-mail: lujiayi@buaa.edu.cn). 6 Yaoguang Cao is with the School of Transportation Science and Engineering, Beihang University, Beijing, China (e-mail: caoyaoguang@buaa.edu.cn).He is the corresponding author of this paper.…”

“…In order to optimize computing power issues, Rosmannet al proposes the elastic band algorithm, which regards the preset path as a constrained elastic rubber band that contracts by a balance of internal and external forces while maintaining a safe distance from obstacles [5] [6]. Nevertheless, the algorithm's performance in strict environments is limited due to its use of soft constraints.…”