TY - JOUR
T1 - SLAV-Sim: A Framework for Self-Learning Autonomous Vehicle Simulation
T2 - SLAV-Sim
AU - Crewe, Jacob
AU - HUMNABADKAR, ADITYA SUNIL
AU - LIU, YONGHUAI
AU - AHMED, AMR
AU - BEHERA, ARDHENDU
PY - 2023/10/23
Y1 - 2023/10/23
N2 - With the advent of autonomous vehicles, sensors and algorithm testing have become crucial parts of the autonomous vehicle development cycle. Having access to real-world sensors and vehicles is a dream for researchers and small-scale original equipment manufacturers (OEMs) due to the software and hardware development life-cycle duration and high costs. Therefore, simulator-based virtual testing has gained traction over the years as the preferred testing method due to its low cost, efficiency, and effectiveness in executing a wide range of testing scenarios. Companies like ANSYS and NVIDIA have come up with robust simulators, and open-source simulators such as CARLA have also populated the market. However, there is a lack of lightweight and simple simulators catering to specific test cases. In this paper, we introduce the SLAV-Sim, a lightweight simulator that specifically trains the behaviour of a self-learning autonomous vehicle. This simulator has been created using the Unity engine and provides an end-to-end virtual testing framework for different reinforcement learning (RL) algorithms in a variety of scenarios using camera sensors and raycasts.
AB - With the advent of autonomous vehicles, sensors and algorithm testing have become crucial parts of the autonomous vehicle development cycle. Having access to real-world sensors and vehicles is a dream for researchers and small-scale original equipment manufacturers (OEMs) due to the software and hardware development life-cycle duration and high costs. Therefore, simulator-based virtual testing has gained traction over the years as the preferred testing method due to its low cost, efficiency, and effectiveness in executing a wide range of testing scenarios. Companies like ANSYS and NVIDIA have come up with robust simulators, and open-source simulators such as CARLA have also populated the market. However, there is a lack of lightweight and simple simulators catering to specific test cases. In this paper, we introduce the SLAV-Sim, a lightweight simulator that specifically trains the behaviour of a self-learning autonomous vehicle. This simulator has been created using the Unity engine and provides an end-to-end virtual testing framework for different reinforcement learning (RL) algorithms in a variety of scenarios using camera sensors and raycasts.
KW - Simulators
KW - Reinforcement Learning
KW - Autonomous Vehicle
KW - Vehicle Behaviour Modelling
KW - Testing Platform
KW - testing platform
KW - vehicle behaviour modelling
KW - autonomous vehicle
KW - simulators
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85175275837&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85175275837&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/40aa9f9a-8866-3b0d-977e-d2c0221a5f41/
U2 - 10.3390/s23208649
DO - 10.3390/s23208649
M3 - Article (journal)
SN - 1424-8220
VL - 23
SP - 1
EP - 22
JO - Sensors
JF - Sensors
IS - 20
M1 - 8649
ER -