A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

When will automated vehicles come onto the market? This question has puzzled the automotive industry and society for years. The technology and its implementation have made rapid progress over the last decade, but the challenge of how to prove the safety of these systems has not yet been solved. Since a market launch without proof of safety would neither be accepted by society nor by legislators, much time and many resources have been invested into safety assessment in recent years in order to develop new approaches for an efficient assessment. This paper therefore provides an overview of various approaches, and gives a comprehensive survey of the so-called scenario-based approach. The scenario-based approach is a promising method, in which individual traffic situations are typically tested by means of virtual simulation. Since an infinite number of different scenarios can theoretically occur in real-world traffic, even the scenario-based approach leaves the question unanswered as to how to break these down into a finite set of scenarios, and find those which are representative in order to render testing more manageable. This paper provides a comprehensive literature review of related safety-assessment publications that deal precisely with this question. Therefore, this paper develops a novel taxonomy for the scenario-based approach, and classifies all literature sources. Based on this, the existing methods will be compared with each other and, as one conclusion, the alternative concept of formal verification will be combined with the scenario-based approach. Finally, future research priorities are derived.

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“…An overview of available data sets can be found in [35], [36]. Zhu et al [37] also show an overview of data sets and try to unify them.…”

Section: ) Sources For Scenariosmentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

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“…The scores of agents are evaluated as a function of the aggregated distance travelled in different circuits, and total points discounted due to infractions. Recent large scale data collection on human-driven cars have lead to a data driven approach using time series data available from the GPU and IMU which were later used to extract driving primitives using unsupervised learning methods such as clustering or Bayesian optimisation [102].…”

Section: Simulator and Scenario Generation Toolsmentioning

confidence: 99%

Deep Reinforcement Learning for Autonomous Driving: A Survey

Kiran¹,

Sobh²,

Talpaert³

et al. 2020

Preprint

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With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms, provides a taxonomy of automated driving tasks where (D)RL methods have been employed, highlights the key challenges algorithmically as well as in terms of deployment of real world autonomous driving agents, the role of simulators in training agents, and finally methods to evaluate, test and robustifying existing solutions in RL and imitation learning.

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“…Our experience also shows that the front view recording using a smartphone camera generates 16 GB data per a hour. Furthermore, recent autonomous driving solutions utilize heterogeneous driving data [17], which exacerbates the data volume problem, resulting in substantial storage and network bandwidth costs.…”

Section: Introductionmentioning

confidence: 99%

CoDR: Correlation-Based Data Reduction Scheme for Efficient Gathering of Heterogeneous Driving Data

Park

Chung

Choi

2020

Sensors

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A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driving data, such as traffic conditions, driver behavior, vehicle status and location information. However, these different types of driving data easily become more than tens of GB per day, forming a significant hurdle due to the storage and network cost. To address this problem, this paper proposes a novel scheme, called CoDR, which can reduce data volume by considering the correlations among heterogeneous driving data. Among heterogeneous datasets, CoDR first chooses one set as a pivot data. Then, according to the objective of data collection, it identifies data ranges relevant to the objective from the pivot dataset. Finally, it investigates correlations among sets, and reduces data volume by eliminating irrelevant data from not only the pivot set but also other remaining datasets. CoDR gathers four heterogeneous driving datasets: two videos for front view and driver behavior, OBD-II and GPS data. We show that CoDR decreases data volume by up to 91%. We also present diverse analytical results that reveal the correlations among the four datasets, which can be exploited usefully for edge computing to reduce data volume on the spot.

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A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

Cited by 6 publications

References 20 publications

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Deep Reinforcement Learning for Autonomous Driving: A Survey

CoDR: Correlation-Based Data Reduction Scheme for Efficient Gathering of Heterogeneous Driving Data

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