Autonomous Vehicles that Alert Humans to Take-Over Controls: Modeling with Real-World Data

Rangesh, Akshay; Deo, Nachiket; Greer, Ross; Gunaratne, Pujitha; Trivedi, Mohan M.

doi:10.48550/arxiv.2104.11489

Cited by 2 publications

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“…We train and evaluate our model using a real world dataset of control transitions captured using a commercially available conditionally autonomous vehicle. This work is an extension of our prior work [3], with three new contributions:…”

Section: Introductionmentioning

confidence: 94%

“…The driver could perform multiple plausible sequences of actions after the issued TOR. To model this, we extend the model proposed in [3] to output a multimodal distribution over TOT. 3) Extensive evaluation: We present a more extensive set of ablation experiments, particularly focused on the above two contributions.…”

Section: Introductionmentioning

confidence: 99%

“…3) Extensive evaluation: We present a more extensive set of ablation experiments, particularly focused on the above two contributions. We also present additional qualitative analysis of TOT estimates beyond [3].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation

Rangesh¹,

Deo²,

Greer³

et al. 2021

Preprint

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Understanding occupant-vehicle interactions by modeling control transitions is important to ensure safe approaches to passenger vehicle automation. Models which contain contextual, semantically meaningful representations of driver states can be used to determine the appropriate timing and conditions for transfer of control between driver and vehicle. However, such models rely on real-world control take-over data from drivers engaged in distracting activities, which is costly to collect. Here, we introduce a scheme for data augmentation for such a dataset. Using the augmented dataset, we develop and train take-over time (TOT) models that operate sequentially on mid and high-level features produced by computer vision algorithms operating on different driver-facing camera views, showing models trained on the augmented dataset to outperform the initial dataset. The demonstrated model features encode different aspects of the driver state, pertaining to the face, hands, foot and upper body of the driver. We perform ablative experiments on feature combinations as well as model architectures, showing that a TOT model supported by augmented data can be used to produce continuous estimates of take-over times without delay, suitable for complex real-world scenarios.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation

Rangesh¹,

Deo²,

Greer³

et al. 2021

Preprint

Self Cite

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“…Most of these studies are conducted in simulators in rural scenarios with low traffic density [4]. Existing approaches are focused on measuring the reaction time after the TOR signal and they validate their proposals in simulators with less sensory power than the one provided by CARLA or utilise real scenarios with very constrained use cases [19].…”

Section: Related Workmentioning

confidence: 99%

Driver Take-Over Behaviour Study Based on Gaze Focalization and Vehicle Data in CARLA Simulator

Araluce

Bergasa

Ocaña

et al. 2022

Sensors

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Autonomous vehicles are the near future of the automobile industry. However, until they reach Level 5, humans and cars will share this intermediate future. Therefore, studying the transition between autonomous and manual modes is a fascinating topic. Automated vehicles may still need to occasionally hand the control to drivers due to technology limitations and legal requirements. This paper presents a study of driver behaviour in the transition between autonomous and manual modes using a CARLA simulator. To our knowledge, this is the first take-over study with transitions conducted on this simulator. For this purpose, we obtain driver gaze focalization and fuse it with the road’s semantic segmentation to track to where and when the user is paying attention, besides the actuators’ reaction-time measurements provided in the literature. To track gaze focalization in a non-intrusive and inexpensive way, we use a method based on a camera developed in previous works. We devised it with the OpenFace 2.0 toolkit and a NARMAX calibration method. It transforms the face parameters extracted by the toolkit into the point where the user is looking on the simulator scene. The study was carried out by different users using our simulator, which is composed of three screens, a steering wheel and pedals. We distributed this proposal in two different computer systems due to the computational cost of the simulator based on the CARLA simulator. The robot operating system (ROS) framework is in charge of the communication of both systems to provide portability and flexibility to the proposal. Results of the transition analysis are provided using state-of-the-art metrics and a novel driver situation-awareness metric for 20 users in two different scenarios.

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Autonomous Vehicles that Alert Humans to Take-Over Controls: Modeling with Real-World Data

Cited by 2 publications

References 0 publications

Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation

Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation

Driver Take-Over Behaviour Study Based on Gaze Focalization and Vehicle Data in CARLA Simulator

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