A machine learning environment for evaluating autonomous driving software

• URL: http://arxiv.org/abs/2003.03576v1
• Date: Sat, 7 Mar 2020 13:05:03 GMT
• Title: A machine learning environment for evaluating autonomous driving software
• Authors: Jussi Hanhirova, Anton Debner, Matias Hyypp\"a, Vesa Hirvisalo
• Abstract summary: We present a machine learning environment for detecting autonomous vehicle corner case behavior. Our environment is based on connecting the CARLA simulation software to machine learning framework and custom AI client software. Our system can search for corner cases where the vehicle AI is unable to correctly understand the situation.
• Score: 1.6516902135723865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous vehicles need safe development and testing environments. Many traffic scenarios are such that they cannot be tested in the real world. We see hybrid photorealistic simulation as a viable tool for developing AI (artificial intelligence) software for autonomous driving. We present a machine learning environment for detecting autonomous vehicle corner case behavior. Our environment is based on connecting the CARLA simulation software to TensorFlow machine learning framework and custom AI client software. The AI client software receives data from a simulated world via virtual sensors and transforms the data into information using machine learning models. The AI clients control vehicles in the simulated world. Our environment monitors the state assumed by the vehicle AIs to the ground truth state derived from the simulation model. Our system can search for corner cases where the vehicle AI is unable to correctly understand the situation. In our paper, we present the overall hybrid simulator architecture and compare different configurations. We present performance measurements from real setups, and outline the main parameters affecting the hybrid simulator performance.

Related papers

• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• Autonomous Driving Simulator based on Neurorobotics Platform [11.25880077022107]
  There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive. At the same time, many of these algorithms need an environment to train and optimize. This report will start with a little research on the Neurorobotics Platform and analyze the potential and possibility of developing a new simulator to achieve the true real-world simulation goal.
  arXiv  Detail & Related papers  (2022-12-31T01:12:27Z)
• Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning [2.467408627377504]
  We introduce a framework (perception, planning, and control) in a real-world driving environment that transfers the real-world environments into gaming environments. We propose variations of existing Reinforcement Learning (RL) algorithms in a multi-agent setting to learn and execute the discrete control in real-world environments.
  arXiv  Detail & Related papers  (2022-11-29T04:24:03Z)
• Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data [56.49494318285391]
  We present Sim2Seg, a re-imagining of RCAN that crosses the visual reality gap for off-road autonomous driving. This is done by learning to translate randomized simulation images into simulated segmentation and depth maps. This allows us to train an end-to-end RL policy in simulation, and directly deploy in the real-world.
  arXiv  Detail & Related papers  (2022-10-25T17:50:36Z)
• Data generation using simulation technology to improve perception mechanism of autonomous vehicles [0.0]
  We will demonstrate the effectiveness of combining data gathered from the real world with data generated in the simulated world to train perception systems. We will also propose a multi-level deep learning perception framework that aims to emulate a human learning experience.
  arXiv  Detail & Related papers  (2022-07-01T03:42:33Z)
• Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation [98.51313127382937]
  We focus on the use of labels in the synthetic domain alone. Our approach introduces both a way to learn neural-invariant representations and a theoretically inspired view on how to sample the data from the simulator. We showcase our approach on the bird's-eye-view vehicle segmentation task with multi-sensor data.
  arXiv  Detail & Related papers  (2021-11-15T18:37:43Z)
• SimNet: Learning Reactive Self-driving Simulations from Real-world Observations [10.035169936164504]
  We present an end-to-end trainable machine learning system capable of realistically simulating driving experiences. This can be used for the verification of self-driving system performance without relying on expensive and time-consuming road testing.
  arXiv  Detail & Related papers  (2021-05-26T05:14:23Z)
• DriveGAN: Towards a Controllable High-Quality Neural Simulation [147.6822288981004]
  We introduce a novel high-quality neural simulator referred to as DriveGAN. DriveGAN achieves controllability by disentangling different components without supervision. We train DriveGAN on multiple datasets, including 160 hours of real-world driving data.
  arXiv  Detail & Related papers  (2021-04-30T15:30:05Z)
• Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction [88.0416857308144]
  We propose an alternative to sensor simulation, as sensor simulation is expensive and has large domain gaps. We directly simulate the outputs of the self-driving vehicle's perception and prediction system, enabling realistic motion planning testing.
  arXiv  Detail & Related papers  (2020-08-13T17:20:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.