Browse Publications Technical Papers 2017-01-1406

The Robustly-Safe Automated Driving System for Enhanced Active Safety 2017-01-1406

Road safety is one of the major concerns for automated vehicles. In order for these vehicles to interact safely and efficiently with the other road participants, the behavior of the automated vehicles should be carefully designed. Liu and Tomizuka proposed the Robustly-safe Automated Driving system (ROAD) which prevents or minimizes occurrences of collisions of the automated vehicle with other road participants while maintaining efficiency. In this paper, a set of design principles are elaborated as an extension of the previous work, including robust perception and cognition algorithms for environment monitoring and high level decision making and low level control algorithms for safe maneuvering of the automated vehicle. The autonomous driving problem in mixed traffic is posed as a stochastic optimization problem, which is solved by 1) behavior classification and trajectory prediction of other road participants, and 2) a unique parallel planner architecture which addresses the efficiency goal in the long term and the safety goal in the short term separately. Moreover, a python-based high fidelity simulation system is developed and extensive simulations are performed to evaluate the effectiveness of the proposed algorithm, where both high level decision making and low level vehicle regulation are considered. Two typical scenarios are studied, driving on freeway and driving in unstructured environments such as parking lots. In the simulation, multiple moving agents representing surrounding vehicles and pedestrians are added to the environment, some of which are controlled by human subjects in order to test the real time response of the automated vehicle.


Subscribers can view annotate, and download all of SAE's content. Learn More »


Members save up to 16% off list price.
Login to see discount.
Special Offer: Download multiple Technical Papers each year? TechSelect is a cost-effective subscription option to select and download 12-100 full-text Technical Papers per year. Find more information here.
We also recommend:

Region Proposal Technique for Traffic Light Detection Supplemented by Deep Learning and Virtual Data


View Details


Analysis and Mathematical Modeling of Car-Following Behavior of Automated Vehicles for Safety Evaluation


View Details


Multilevel Concept for Verification of Automated Driving Systems by Using Model in the Loop Simulations in Early Development Phases


View Details