Human-inspired autonomous driving: A survey

Plebe, Alice; Svensson, Henrik; Mahmoud, Sara; Da Lio, Mauro

doi:10.1016/j.cogsys.2023.101169

Cited by 3 publications

(2 citation statements)

References 105 publications

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“…In the evolving landscape of autonomous driving, a likely scenario envisages a cooperative yet competitive interaction between human drivers and autonomous vehicles, each striving for their own advantages on the road [7]. This dynamic interplay signifies scenarios where human intuition should complement the precision of autonomous systems, fostering harmonious coexistence [8]. Beyond automation, the connectivity of vehicles plays a crucial role, serving as a foundational element for smart cities [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Insights into Simulated Smart Mobility on Roundabouts: Achievements, Lessons Learned, and Steps Ahead

Tumminello,

Macioszek,

Granà

2024

Sustainability

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This paper explores the domain of intelligent transportation systems, specifically focusing on roundabouts as potential solutions in the context of smart mobility. Roundabouts offer a safer and more efficient driving environment compared to other intersections, thanks to their curvilinear trajectories promoting speed control and lower vehicular speeds for traffic calming. The synthesis review supported the authors in presenting current knowledge and emerging needs in roundabout design and evaluation. A focused examination of the models and methods used to assess safety and operational performance of roundabout systems was necessary. This is particularly relevant in light of new challenges posed by the automotive market and the influence of vehicle-to-vehicle communication on the conceptualization and design of this road infrastructure. Two case studies of roundabouts were analyzed in Aimsun to simulate the increasing market penetration rates of connected and autonomous vehicles (CAVs) and their traffic impacts. Through microscopic traffic simulation, the research evaluated safety and performance efficiency advancements in roundabouts. The paper concludes by outlining areas for further research and evolving perspectives on the role of roundabouts in the transition toward connected and autonomous vehicles and infrastructures.

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Section: Introductionmentioning

confidence: 99%

“…tuition should complement the precision of autonomous systems, fostering harmonious coexistence [8]. Beyond automation, the connectivity of vehicles plays a crucial role, serving as a foundational element for smart cities [9,10].…”

Section: Introductionmentioning

confidence: 99%

Insights into Simulated Smart Mobility on Roundabouts: Achievements, Lessons Learned, and Steps Ahead

Tumminello,

Macioszek,

Granà

2024

Sustainability

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Bio-inspired circular latent spaces to estimate objects' rotations

Plebe,

Da Lio

2023

Front. Comput. Neurosci.

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This paper proposes a neural network model that estimates the rotation angle of unknown objects from RGB images using an approach inspired by biological neural circuits. The proposed model embeds the understanding of rotational transformations into its architecture, in a way inspired by how rotation is represented in the ellipsoid body of Drosophila. To effectively capture the cyclic nature of rotation, the network's latent space is structured in a circular manner. The rotation operator acts as a shift in the circular latent space's units, establishing a direct correspondence between shifts in the latent space and angular rotations of the object in the world space. Our model accurately estimates the difference in rotation between two views of an object, even for categories of objects that it has never seen before. In addition, our model outperforms three state-of-the-art convolutional networks commonly used as the backbone for vision-based models in robotics.

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Towards the Interpretation of Customizable Imitation Learning of Human Driving Behavior in Mixed Traffic Scenarios

Rebling,

Kriesten,

Nenninger

2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">With further development of autonomous vehicles additional challenges appear. One of these challenges arises in the context of mixed traffic scenarios where automated and autonomous vehicles coexist with manually operated vehicles as well as other road users such as cyclists and pedestrians. In this evolving landscape, understanding, predicting, and mimicking human driving behavior is becoming not only a challenging but also a compelling facet of autonomous driving research. This is necessary not only for safety reasons, but also to promote trust in artificial intelligence (AI), especially in self-driving cars where trust is often compromised by the opacity of neural network models. The central goal of this study is therefore to address this trust issue. A common approach to imitate human driving behavior through expert demonstrations is imitation learning (IL). However, balancing performance and explainability in these models is a major challenge. To efficiently generate training data, researchers have turned to simulation environments because collecting data in the real world is not only costly and time-consuming, but also potentially dangerous. Simulations provide a controlled and scalable platform for training reliable AI agents. The goal of this research is to bridge the gap between IL, explainability, and trust in AI-controlled vehicles navigating mixed traffic scenarios. Our proposed approach involves a novel fusion of explainable neural network architectures with parameterization techniques that enable precise control of learned driving behavior. By using advanced simulation environments and a variety of interconnected simulators that provide different levels of immersion, we intend to collect a wide range of information and training data. This wealth of knowledge will allow us to draw conclusions about the effectiveness of these simulator methods and ensure the generalizability of our model.</div></div>

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Human-inspired autonomous driving: A survey

Cited by 3 publications

References 105 publications

Insights into Simulated Smart Mobility on Roundabouts: Achievements, Lessons Learned, and Steps Ahead

Insights into Simulated Smart Mobility on Roundabouts: Achievements, Lessons Learned, and Steps Ahead

Bio-inspired circular latent spaces to estimate objects' rotations

Towards the Interpretation of Customizable Imitation Learning of Human Driving Behavior in Mixed Traffic Scenarios

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