A Fail-Operational Control Architecture Approach and Dead-Reckoning Strategy in Case of Positioning Failures

Presently, in the event of a failure in Automated Driving Systems, control architectures rely on hardware redundancies over software solutions to assure reliability or wait for human interaction in takeover requests to achieve a minimal risk condition. As user confidence and final acceptance of this novel technology are strongly related to enabling safe states, automated fall-back strategies must be assured as a response to failures while the system is performing a dynamic driving task. In this work, a fail-operational control architecture approach and dead-reckoning strategy in case of positioning failures are developed and presented. A fail-operational system is capable of detecting failures in the last available positioning source, warning the decision stage to set up a fall-back strategy and planning a new trajectory in real time. The surrounding objects and road borders are considered during the vehicle motion control after failure, to avoid collisions and lane-keeping purposes. A case study based on a realistic urban scenario is simulated for testing and system verification. It shows that the proposed approach always bears in mind both the passenger’s safety and comfort during the fall-back maneuvering execution.This research was funded by AutoDrive within the Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL JU) in collaboration with the European Union’s H2020 Framework Programme (H2020/2014-2020) and National Authorities, under grant agreement number 737469

A Vehicle Simulation Model and Automated Driving Features Validation for Low-Speed High Automation Applications

The low-speed high automation (LSHA) is foreseen as a development path for new types of mobility, improving road safety and addressing transit problems in urban infrastructures. As these automation approaches are still in the development phase, methods to improve their design and validation are required. The use of vehicle simulation models allows reducing significantly the time deployment on real test tracks, which would not consider all the scenarios or complexity related to automated driving features. However, to ensure safety and accuracy while evaluating the proper operation of LSHA features, adequate validation methodologies are mandatory. In this study a two-step validation methodology is proposed: Firstly, an open-loop test set attempts to tune the required vehicle simulation models using experimental data considering also the dynamics of the actuation devices required for vehicle automation. Secondly, a closed-loop test strives to validate the selected automated driving functionality based on test plans, also improving the vehicle dynamics response. To illustrate the methodology, a study case is proposed using an automated Renault Twizy. In the first step, the brake pedal and steering wheel actuators' behavior is modeled, as well as its longitudinal dynamics and turning capacity. Then, in a second step, an LSHA functionality for Traffic Jam Assist based on a Model Predictive Control approach is evaluated and validated. Results demonstrate that the proposed methodology is capable not only to tune vehicle simulation models for automated driving development purposes but also to validate LSHA functionalities

A Fail-Operational Control Architecture Approach and Dead-Reckoning Strategy in Case of Positioning Failures

Presently, in the event of a failure in Automated Driving Systems, control architectures rely on hardware redundancies over software solutions to assure reliability or wait for human interaction in takeover requests to achieve a minimal risk condition. As user confidence and final acceptance of this novel technology are strongly related to enabling safe states, automated fall-back strategies must be assured as a response to failures while the system is performing a dynamic driving task. In this work, a fail-operational control architecture approach and dead-reckoning strategy in case of positioning failures are developed and presented. A fail-operational system is capable of detecting failures in the last available positioning source, warning the decision stage to set up a fall-back strategy and planning a new trajectory in real time. The surrounding objects and road borders are considered during the vehicle motion control after failure, to avoid collisions and lane-keeping purposes. A case study based on a realistic urban scenario is simulated for testing and system verification. It shows that the proposed approach always bears in mind both the passenger’s safety and comfort during the fall-back maneuvering execution

Longitudinal Collision Avoidance Based on Model Predictive Controllers and Fuzzy Inference Systems

Publisher Copyright: © 2020 IEEE.During the last years' research on Collision Avoidance Systems (CAS) is gaining special attention, due to the decrease of on-road accidents. Current commercial systems can reduce the vehicle speed in case of emergencies such as the appearance of obstacles on the road. However, the behavior of commercial systems is frequently too rigid failing to achieve a proper balance between safety and comfort. In this scenario, this work presents a new approach in which the contextual information of the surrounding environment, such as dedicated infrastructure for vulnerable road users or objects in the vicinity, is used to assess the risks through a Fuzzy inference system. Once risks are evaluated the constraints on the controller acting over the longitudinal vehicle motion are established accordingly. The controller uses a Model Predictive Control (MPC) algorithm. The presented approach illustrates the benefits of modulating the constraints of the MPC controller according to the risk assessment. This approach generates a dynamic speed profile smoothing out critical braking scenarios depending on distances to further objects. For validation, a complex urban scenario was simulated. Results show good performance on the speed planner, also allowing an extendable generalization to different road structures and predefined behaviors from maps and perception systems.This work was partly supported by the AutoDrive ECSEL Project with Grant Agreement no. 737469.Peer reviewe