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Towards Semantic Understanding of Surrounding Vehicular Maneuvers: A Panoramic Vision-Based Framework for Real-World Highway Studies

Miklas S. Kristoffersen, Jacob V. Dueholm, Ravi K. Satzoda, Mohan M. Trivedi, Andreas Mogelmose, Thomas B. Moeslund
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Multi-perspective trajectories are estimated and analyzed to extract 14 different events, including potential dangerous behaviors such as overtakes and cut-ins.  ...  The results show the potential use of multiple low-cost visual sensors for semantic understanding around the ego-vehicle.  ...  Trajectory Analysis A map or a list of the dynamics and behaviors of surrounding vehicles is an integral part of understanding what is happening around the ego-vehicle, and why something is happening.  ... 
doi:10.1109/cvprw.2016.197 dblp:conf/cvpr/KristoffersenDS16 fatcat:fvlkl5my4jborp2ye5hn7y4bee

Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations [article]

Priyam Parashar, Akansel Cosgun, Alireza Nakhaei, Kikuo Fujimura
2018 arXiv   pre-print
The linear combination algorithm is informed by analysis of driving data, which also suggests that decision-making algorithms need to consider a trade-off between road-rules and immediate rewards to tackle  ...  This paper presents a learning from demonstration approach to programming safe, autonomous behaviors for uncommon driving scenarios.  ...  10% of the vehicle parked on the road) or Far (vehicle clearly parked on the curb), (c) Direction of Traffic: No traffic (unidirectional road) or Opposing traffic (bidirectional road).  ... 
arXiv:1806.00143v1 fatcat:a22oksqpercbtbkonop7pftsyu

Learning by Watching [article]

Jimuyang Zhang, Eshed Ohn-Bar
2021 arXiv   pre-print
However, such measurements cannot be directly accessed for the non-ego vehicles when learning by watching others.  ...  In contrast, existing techniques for learning to drive preclude such a possibility as they assume direct access to an instrumented ego-vehicle with fully known observations and expert driver actions.  ...  In addition to the agent-centric BEV, we estimate the speed and high-level command of watched agents (v, ĉ) by tracking and comparing their 3D position over time, as observed from the ego-vehicle perspective  ... 
arXiv:2106.05966v1 fatcat:eabm4ktwpje4toirfhd5krtv6q

Performance Measurement Evaluation Framework and Co-Benefit\/Tradeoff Analysis for Connected and Automated Vehicles (CAV) Applications: A Survey

Danyang Tian, Guoyuan Wu, Kanok Boriboonsomsin, Matthew J. Barth
2018 IEEE Intelligent Transportation Systems Magazine  
She is also interested in traffic state prediction models, cooperative and safe lane-changing behavior, and traffic scheduling.  ...  His current research interests include ITS and the Environment, Transportation/Emissions Modeling, Vehicle Activity Analysis, Advanced Navigation Techniques, Electric Vehicle Technology, and Advanced Sensing  ...  driven by on-board sensors and communication technologies, aimed at the ego-vehicle and/or the surrounding traffic.  ... 
doi:10.1109/mits.2018.2842020 fatcat:i7qgxafhvbddvabav3ixredfza

Driver-centric Risk Object Identification [article]

Chengxi Li, Stanley H. Chan, Yi-Ting Chen
2021 arXiv   pre-print
In this work, we propose a novel driver-centric definition of risk, i.e., risky objects influence driver behavior.  ...  A driver-centric Risk Object Identification (ROI) dataset is curated to evaluate the proposed system.  ...  Ego Vehicle (a) Crossing Vehicle Ego Vehicle (b) Crossing Pedestrian Ego Vehicle (c) Parked Vehicle Ego Vehicle (d) Congestion Ego Vehicle (e) Crossing Vehicle Ego Vehicle (f) Crossing  ... 
arXiv:2106.13201v1 fatcat:yusgxiknnvbrxae4ocitlbjdsq

COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [article]

Jiaxun Cui, Hang Qiu, Dian Chen, Peter Stone, Yuke Zhu
2022 arXiv   pre-print
With recent developments of telecommunication technologies, cooperative perception with vehicle-to-vehicle communications has become a promising paradigm to enhance autonomous driving in dangerous or emergency  ...  Optical sensors and learning algorithms for autonomous vehicles have dramatically advanced in the past few years.  ...  Sensitivity analysis on the varying levels of traffic densities in the Left Turn scenario. Figure 5 . 5 Figure 5. Comparison of trajectories in the Left Turn scenario.  ... 
arXiv:2205.02222v1 fatcat:sg7rbexumngivn3acn2etpd4pm

EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments [article]

Wenchao Ding, Lu Zhang, Jing Chen, Shaojie Shen
2021 arXiv   pre-print
Based on the SSC, a safe and smooth trajectory is optimized, complying with the decision provided by the behavior planner.  ...  We validate our planning system in both simulations and real-world dense traffic, and the experimental results show that our EPSILON achieves human-like driving behaviors in highly interactive traffic  ...  Let E t denote the ego-centric local environment at time t, including road structures, traffic signals and the occupancy grid for static obstacles.  ... 
arXiv:2108.07993v1 fatcat:yvhgk33sxbebvn5l5v574dcjn4

Abstracts

2020 IEEE Transactions on Intelligent Vehicles  
The planner is complemented by a high-level behavior planner, which takes into account the occupancy of other traffic participants, the information from the vehicle's perception system, and the risk associated  ...  Safe trajectories are generated by solving, in real-time, a non-linear constrained optimization, formulated as a receding horizon planner that maximizes the ego vehicle's visibility.  ...  Evaluating the similarity levels of driving behavior plays a pivotal role in driving style classification and analysis, thus benefiting the design of human-centric driver assistance systems.  ... 
doi:10.1109/tiv.2020.2971873 fatcat:af7apwjygrajxeenbiwv7grzii

Understanding Dynamic Scenes using Graph Convolution Networks [article]

Sravan Mylavarapu, Mahtab Sandhu, Priyesh Vijayan, K Madhava Krishna, Balaraman Ravindran, Anoop Namboodiri
2020 arXiv   pre-print
We present a novel Multi-Relational Graph Convolutional Network (MRGCN) based framework to model on-road vehicle behaviors from a sequence of temporally ordered frames as grabbed by a moving monocular  ...  Such behavior prediction methods find immediate relevance in a variety of navigation tasks such as behavior planning, state estimation, and applications relating to the detection of traffic violations  ...  Recent research showcase results that understanding on-road vehicle behavior leads to better behavior planners for the ego vehicle [2] .  ... 
arXiv:2005.04437v5 fatcat:mpfc3fv5c5ab5hgk5h5jaantjm

Driving Behavior Explanation with Multi-level Fusion [article]

Hédi Ben-Younes and Éloi Zablocki and Patrick Pérez and Matthieu Cord
2020 arXiv   pre-print
We present BEEF, for BEhavior Explanation with Fusion, a deep architecture which explains the behavior of a trajectory prediction model.  ...  In this work, we focus on generating high-level driving explanations as the vehicle drives.  ...  the ego-vehicle should reach.  ... 
arXiv:2012.04983v1 fatcat:d47h46kdqbhizc3mgw5jha66fu

Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world [article]

Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang, Brandon Amos, Jakob Foerster
2022 arXiv   pre-print
the expert trajectories.  ...  Using open-source trajectory and map data, we construct a simulator to load and replay arbitrary trajectories and scenes from real-world driving data.  ...  Thanks to Scott Ettinger for help in understanding some of the peculiarities of the Waymo Motion Dataset [10] . Thanks to Rachit Singh for help with some of the results analysis scripts.  ... 
arXiv:2206.09889v1 fatcat:wfrbaghdmncdlbniikkyj6ffdi

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

Florin Leon, Marius Gavrilescu
2021 Mathematics  
., anticipating the future trajectories and motion of other vehicles in order to facilitate navigating through various traffic conditions.  ...  The trajectory information is passed through LSTMs to construct three maps for the horizon, neighbors, and ego vehicle.  ...  Past and future positions are represented in an ego car-centric coordinate system.  ... 
doi:10.3390/math9060660 fatcat:qvikrr32tzd7fnjzs22u3ago4m

The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenX [article]

Alexander Tenbrock, Alexander König, Thomas Keutgens, Julian Bock, Hendrik Weber, Robert Krajewski, Adrian Zlocki
2021 arXiv   pre-print
To overcome this problem, we incorporate the exposure and consider the heterogeneous behavior of the traffic participants by extracting concrete scenarios according to Regulation's scenario definition  ...  We compare the trajectories to examine the similarity of the scenarios in the simulation to the recorded scenarios.  ...  As it offers recordings of naturalistic traffic behavior, highD [12] , a large-scale trajectory dataset from German Autobahn by fka and ika, is utilized to get the exposure on the behavior of the traffic  ... 
arXiv:2103.09772v1 fatcat:owib5ue2dbeslovlgws2tb67ui

Investigating pedal errors and multi-modal effects: Driving testbed development and experimental analysis

Cuong Tran, Anup Doshi, Mohan M. Trivedi
2012 2012 15th International IEEE Conference on Intelligent Transportation Systems  
We will then discuss our analysis towards understanding some factors influencing driver pedal errors including driver workload, sequential effect, and cue modality (i.e. audio visual stimuli) as well as  ...  In this paper, we introduce two multi-modal driving testbeds (including both a real-world vehicle and a driving simulator) that we have been developing for years in our laboratory.  ...  (e.g. other vehicles, obstacles, traffic signs), and driver (e.g. driver behavior and cognitive state).  ... 
doi:10.1109/itsc.2012.6338908 dblp:conf/itsc/0001DT12 fatcat:hffx36tuevbwzc2cvgrxgoey2y

Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions [article]

Joey Hong, Benjamin Sapp, James Philbin
2019 arXiv   pre-print
efforts which provide highly accurate 3D states of agents with rich attributes, and (2) detailed and accurate semantic maps of the environment (lanes, traffic lights, crosswalks, etc).  ...  We introduce a novel dataset providing industry-grade rich perception and semantic inputs, and empirically show we can effectively learn fundamentals of driving behavior.  ...  Overall, trajectories have plausibly learned traffic rules: lane-keeping, traffic light obeyance, following behavior, and even the illegal ones are specious.  ... 
arXiv:1906.08945v1 fatcat:dbxqgiv2pnb43ky4yioabnyw6i
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