DeepAI AI Chat
Log In Sign Up

Rapidly-exploring Random Trees-based Test Generation for Autonomous Vehicles

by   Cumhur Erkan Tuncali, et al.
Arizona State University

Autonomous vehicles are in an intensive research and development stage, and the organizations developing these systems are targeting to deploy them on public roads in a very near future. One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. In this paper, an automated test generation approach that utilizes Rapidly-exploring Random Trees is presented. A comparison of the proposed approach with an optimization-guided falsification approach from the literature is provided. Furthermore, a cost function that guides the test generation toward almost-avoidable collisions or near-misses is proposed.


page 8

page 12

page 13

page 15

page 16


Synthesis of Different Autonomous Vehicles Test Approaches

Currently, the most prevalent way to evaluate an autonomous vehicle is t...

Towards Mechatronics Approach of System Design, Verification and Validation for Autonomous Vehicles

Modern-day autonomous vehicles are increasingly becoming complex multidi...

Designing Autonomous Vehicles: Evaluating the Role of Human Emotions and Social Norms

Humans are going to delegate the rights of driving to the autonomous veh...

Simulation and Model Checking for Close to Realtime Overtaking Planning

Fast and reliable trajectory planning is a key requirement of autonomous...

Park4U Mate: Context-Aware Digital Assistant for Personalized Autonomous Parking

People park their vehicle depending on interior and exterior contexts. T...

Accelerated RRT* and its evaluation on Autonomous Parking

Finding a collision-free path for autonomous parking is usually performe...