Towards behaviour based testing to understand the black box of autonomous cars
F Utesch, A Brandies, P Pekezou Fouopi… - European Transport …, 2020 - Springer
Background Autonomous cars could make traffic safer, more convenient, efficient and
sustainable. They promise the convenience of a personal taxi, without the need for a human …
sustainable. They promise the convenience of a personal taxi, without the need for a human …
Deep learning for self-driving cars: Chances and challenges
Q Rao, J Frtunikj - Proceedings of the 1st international workshop on …, 2018 - dl.acm.org
Artificial Intelligence (AI) is revolutionizing the modern society. In the automotive industry,
researchers and developers are actively pushing deep learning based approaches for …
researchers and developers are actively pushing deep learning based approaches for …
Predict the risk feeling for drivers of autonomous cars: An application of deep learning methods
C Gandrez, F Mantelet, A Aoussat… - International Journal on …, 2023 - Springer
Simulation is used to assess safety provided by autonomous vehicle algorithms. However,
safety derived by computation systems can have significant gaps with driver's feeling of …
safety derived by computation systems can have significant gaps with driver's feeling of …
Safe deep neural network-driven autonomous vehicles using software safety cages
Deep learning is a promising class of techniques for controlling an autonomous vehicle.
However, functional safety validation is seen as a critical issue for these systems due to the …
However, functional safety validation is seen as a critical issue for these systems due to the …
Combining Deep Learning with traditional algorithms in autonomous cars
A Falk, D Granqvist - 2017 - odr.chalmers.se
Research of autonomous technologies in modern vehicles are being conducted as never
before. For a long time, traditional computer vision based algorithms has been the primary …
before. For a long time, traditional computer vision based algorithms has been the primary …
Autonomous cars: technical challenges and a solution to blind spot
HM Thakurdesai, JV Aghav - Advances in Computational Intelligence and …, 2021 - Springer
Automotive industry is progressing forward toward the future, where the role of driver is
becoming smaller and leading to become ideally driverless. Designing a fully driverless car …
becoming smaller and leading to become ideally driverless. Designing a fully driverless car …
An overview of deep learning techniques for autonomous driving vehicles
VM Deshmukh, B Rajalakshmi… - … on Smart Systems …, 2022 - ieeexplore.ieee.org
The data to train autonomous cars was not so abundant before a few years. After Waymo
released their driving data, it is widely used in academic research. It consists of a huge …
released their driving data, it is widely used in academic research. It consists of a huge …
Virtual testing in automated driving systems certification. A longitudinal dynamics validation example
The safety validation of Automated Driving Systems (ADSs) needs a combination of tools to
ensure testing in a broad range of traffic scenarios. Among the others, virtual testing is …
ensure testing in a broad range of traffic scenarios. Among the others, virtual testing is …
Paracosm: A language and tool for testing autonomous driving systems
Systematic testing of autonomous vehicles operating in complex real-world scenarios is a
difficult and expensive problem. We present Paracosm, a reactive language for writing test …
difficult and expensive problem. We present Paracosm, a reactive language for writing test …
Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges
Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …