Adding navigation to the equation: Turning decisions for end-to-end vehicle control

Hubschneider, Christian; Bauer, André; Weber, Michael; Zöllner, J. Marius

doi:10.1109/itsc.2017.8317923

Cited by 37 publications

(18 citation statements)

References 9 publications

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“…Figure 3 illustrates the impact of different lateral offsets on the projection: on 3c and 3e, the deformations are barely visible, on 3d and 3f they can be seen under the horizon on vertical objects. Globally, the appearance is more realistic than the shear proposed in [11]. We argue that these deformations have a limited impact on the training compared to the improvement of doing label augmentation.…”

Section: A Label Augmentationmentioning

confidence: 88%

End to End Vehicle Lateral Control Using a Single Fisheye Camera

Toromanoff

Wirbel

Wilhelm

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Convolutional neural networks are commonly used to control the steering angle for autonomous cars. Most of the time, multiple long range cameras are used to generate lateral failure cases. In this paper we present a novel model to generate this data and label augmentation using only one short range fisheye camera. We present our simulator and how it can be used as a consistent metric for lateral end-to-end control evaluation. Experiments are conducted on a custom dataset corresponding to more than 10000 km and 200 hours of open road driving. Finally we evaluate this model on real world driving scenarios, open road and a custom test track with challenging obstacle avoidance and sharp turns. In our simulator based on real-world videos, the final model was capable of more than 99% autonomy on urban road.

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Section: A Label Augmentationmentioning

confidence: 88%

End to End Vehicle Lateral Control Using a Single Fisheye Camera

Toromanoff

Wirbel

Wilhelm

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Deep learning advances have reignited interest in conditional imitation learning for autonomous driving [51]. ALVINN utilized a conditional order to display an E2E network for lanes following vacant highways, which monitored the steering angle from a single camera [52]. It learned longitudinal and transverse control through CIL, using a remote-control vehicle to execute route commands in a static environment [53].…”

Section: Related Workmentioning

confidence: 99%

Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality

Haris

Głowacz

2022

Sensors

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The ability of artificial intelligence to drive toward an intended destination is a key component of an autonomous vehicle. Different paradigms are now being employed to address artificial intelligence advancement. On the one hand, modular pipelines break down the driving model into submodels, such as perception, maneuver planning and control. On the other hand, we used the end-to-end driving method to assign raw sensor data directly to vehicle control signals. The latter is less well-studied but is becoming more popular since it is easier to use. This article focuses on end-to-end autonomous driving, using RGB pictures as the primary sensor input data. The autonomous vehicle is equipped with a camera and active sensors, such as LiDAR and Radar, for safe navigation. Active sensors (e.g., LiDAR) provide more accurate depth information than passive sensors. As a result, this paper examines whether combining the RGB from the camera and active depth information from LiDAR has better results in end-to-end artificial driving than using only a single modality. This paper focuses on the early fusion of multi-modality and demonstrates how it outperforms a single modality using the CARLA simulator.

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“…To take into account high level control, a branch architecture producing as many output as available highlevel commands (Figure 2) is often used [16], [2], [8]. It is more effective in forcing the agent to take into account the command [2] than a simple concatenation of the command to the neural network input [19].…”

Section: Related Workmentioning

confidence: 99%

Evaluating Robustness over High Level Driving Instruction for Autonomous Driving

Carton¹,

Filliat²,

Rabarisoa³

et al. 2021

Preprint

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In recent years, we have witnessed increasingly high performance in the field of autonomous end-toend driving. In particular, more and more research is being done on driving in urban environments, where the car has to follow high level commands to navigate. However, few evaluations are made on the ability of these agents to react in an unexpected situation. Specifically, no evaluations are conducted on the robustness of driving agents in the event of a bad high-level command. We propose here an evaluation method, namely a benchmark that allows to assess the robustness of an agent, and to appreciate its understanding of the environment through its ability to keep a safe behavior, regardless of the instruction.

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Adding navigation to the equation: Turning decisions for end-to-end vehicle control

Cited by 37 publications

References 9 publications

End to End Vehicle Lateral Control Using a Single Fisheye Camera

End to End Vehicle Lateral Control Using a Single Fisheye Camera

Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality

Evaluating Robustness over High Level Driving Instruction for Autonomous Driving

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