Neurocognitive and traffic based handover strategies
- URL: http://arxiv.org/abs/2101.10186v1
- Date: Fri, 22 Jan 2021 14:46:21 GMT
- Title: Neurocognitive and traffic based handover strategies
- Authors: Andreas Otte, Jonas Vogt, Jens Staub, Niclas Wolniak and Horst Wieker
- Abstract summary: The goal is to combine neurocognitive measurement of the drivers state and the static and dynamic traffic related data to develop an interpretation of the current situation.
This situation analysis should be the basis for the determination of the best takeover point.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The level of automation in vehicles will significantly increase over the next
decade. As automation will become more and more common, vehicles will not be
able to master all traffic related situations for a long time by themselves. In
such situations, the driver must take over and steer the vehicle through the
situation. One of the important questions is when the takeover should be
performed. Many decisive factors must be considered. On the one hand, the
current traffic situation including roads, traffic light and other road users,
especially vulnerable road users, and on the other hand, the state of the
driver must be considered. The goal is to combine neurocognitive measurement of
the drivers state and the static and dynamic traffic related data to develop an
interpretation of the current situation. This situation analysis should be the
basis for the determination of the best takeover point.
Related papers
- Holistic view of the road transportation system based on real-time data sharing mechanism [9.503118311645515]
This paper constructs a space-time global view of the road traffic system based on a real-time sharing mechanism.
It enables both road users and managers to timely access the driving intentions of nearby vehicles and the real-time status of road infrastructure.
arXiv Detail & Related papers (2024-07-03T15:10:05Z) - Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks [9.485363025495225]
We present a novel semantic traffic control system that entrusts semantic encoding responsibilities to the vehicles themselves.
This system processes driving decisions obtained from a Reinforcement Learning (RL) agent, streamlining the decision-making process.
arXiv Detail & Related papers (2024-06-26T20:12:48Z) - Optimized Detection and Classification on GTRSB: Advancing Traffic Sign
Recognition with Convolutional Neural Networks [0.0]
This paper presents an innovative approach leveraging CNNs that achieves an accuracy of nearly 96%.
It highlights the potential for even greater precision through advanced localization techniques.
arXiv Detail & Related papers (2024-03-13T06:28:37Z) - DenseLight: Efficient Control for Large-scale Traffic Signals with Dense
Feedback [109.84667902348498]
Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network.
Most prior TSC methods leverage deep reinforcement learning to search for a control policy.
We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
arXiv Detail & Related papers (2023-06-13T05:58:57Z) - Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow [76.38515853201116]
Semi-cooperative behaviors are intrinsic properties of human drivers and should be considered for autonomous driving.
New autonomous planners can consider the social value orientation (SVO) of human drivers to generate socially-compliant trajectories.
We present study of implicit semi-cooperative driving where agents deploy a game-theoretic version of iterative best response.
arXiv Detail & Related papers (2023-04-23T16:01:36Z) - Decision Making for Autonomous Driving in Interactive Merge Scenarios
via Learning-based Prediction [39.48631437946568]
This paper focuses on the complex task of merging into moving traffic where uncertainty emanates from the behavior of other drivers.
We frame the problem as a partially observable Markov decision process (POMDP) and solve it online with Monte Carlo tree search.
The solution to the POMDP is a policy that performs high-level driving maneuvers, such as giving way to an approaching car, keeping a safe distance from the vehicle in front or merging into traffic.
arXiv Detail & Related papers (2023-03-29T16:12:45Z) - Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing.
We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
arXiv Detail & Related papers (2022-06-28T17:08:31Z) - Safety-aware Motion Prediction with Unseen Vehicles for Autonomous
Driving [104.32241082170044]
We study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving.
Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map.
Our approach is the first one that can predict the existence of unseen vehicles in most cases.
arXiv Detail & Related papers (2021-09-03T13:33:33Z) - End-to-End Intersection Handling using Multi-Agent Deep Reinforcement
Learning [63.56464608571663]
Navigating through intersections is one of the main challenging tasks for an autonomous vehicle.
In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided.
We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
arXiv Detail & Related papers (2021-04-28T07:54:40Z) - Emergent Road Rules In Multi-Agent Driving Environments [84.82583370858391]
We analyze what ingredients in driving environments cause the emergence of road rules.
We find that two crucial factors are noisy perception and agents' spatial density.
Our results add empirical support for the social road rules that countries worldwide have agreed on for safe, efficient driving.
arXiv Detail & Related papers (2020-11-21T09:43:50Z) - Decoding pedestrian and automated vehicle interactions using immersive
virtual reality and interpretable deep learning [6.982614422666432]
This study investigates pedestrian crossing behaviour, as an important element of urban dynamics that is expected to be affected by the presence of automated vehicles.
Pedestrian wait time behaviour is then analyzed using a data-driven Cox Proportional Hazards (CPH) model.
Results show that the presence of automated vehicles on roads, wider lane widths, high density on roads, limited sight distance, and lack of walking habits are the main contributing factors to longer wait times.
arXiv Detail & Related papers (2020-02-18T01:30:29Z)
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.