scholarly journals Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures

2020 ◽  
Author(s):  
Allen Wang ◽  
Xin Huang ◽  
Ashkan Jasour ◽  
Brian Williams
Keyword(s):  
Risk Assessment ◽  
Autonomous Vehicles

scholarly journals Fluid-inspired field representation for risk assessment in road scenes

2020 ◽  
Vol 6 (4) ◽  
pp. 401-415
Author(s):  
Xuanpeng Li ◽  
Lifeng Zhu ◽  
Qifan Xue ◽  
Dong Wang ◽  
Yongjie Jessica Zhang
Keyword(s):  
Risk Assessment ◽  
Intelligent Agents ◽  
Autonomous Vehicles ◽  
Diffusion Processes ◽  
Fluid Model ◽  
Tracking Error ◽  
Dynamic Environment ◽  
Collision Risk ◽  
Field Representation ◽  
Sensing Technology

AbstractPrediction of the likely evolution of traffic scenes is a challenging task because of high uncertainties from sensing technology and the dynamic environment. It leads to failure of motion planning for intelligent agents like autonomous vehicles. In this paper, we propose a fluid-inspired model to estimate collision risk in road scenes. Multi-object states are detected and tracked, and then a stable fluid model is adopted to construct the risk field. Objects’ state spaces are used as the boundary conditions in the simulation of advection and diffusion processes. We have evaluated our approach on the public KITTI dataset; our model can provide predictions in the cases of misdetection and tracking error caused by occlusion. It proves a promising approach for collision risk assessment in road scenes.

Runtime Active Safety Risk-Assessment of Highly Autonomous Vehicles for Safe Nominal Behavior

2020 ◽  
Author(s):  
Swarn Singh Rathour ◽  
Tasuku Ishigooka ◽  
Satoshi Otsuka ◽  
RAUL MARTIN
Keyword(s):  
Risk Assessment ◽  
Autonomous Vehicles ◽  
Active Safety ◽  
Safety Risk

scholarly journals From Traditional to Autonomous Vehicles: A Systematic Review of Data Availability

2021 ◽  
pp. 036119812110575
Author(s):  
Leandro Masello ◽  
Barry Sheehan ◽  
Finbarr Murphy ◽  
German Castignani ◽  
Kevin McDonnell ◽  
...  
Keyword(s):  
Systematic Review ◽  
Risk Assessment ◽  
Transportation Planning ◽  
Road Safety ◽  
Autonomous Vehicles ◽  
Gap Analysis ◽  
Autonomous Driving ◽  
Data Availability ◽  
Principal Characteristics ◽  
Risk Assessment Models

The increasing accessibility of mobility datasets has enabled research in green mobility, road safety, vehicular automation, and transportation planning and optimization. Many stakeholders have leveraged vehicular datasets to study conventional driving characteristics and self-driving tasks. Notably, many of these datasets have been made publicly available, fostering collaboration, scientific comparability, and replication. As these datasets encompass several study domains and contain distinctive characteristics, selecting the appropriate dataset to investigate driving aspects might be challenging. To the best of the authors’ knowledge, this is the first paper that performs a systematic review of a substantial number of vehicular datasets covering various automation levels. In total, 103 datasets have been reviewed, 35 of which focused on naturalistic driving, and 68 on self-driving tasks. The paper gives researchers the possibility of analyzing the datasets’ principal characteristics and their study domains. Most naturalistic datasets have been centered on road safety and driver behavior, although transportation planning and eco-driving have also been studied. Furthermore, datasets for autonomous driving have been analyzed according to their target self-driving tasks. A particular focus has been placed on data-driven risk assessment for the vehicular ecosystem. It is observed that there exists a lack of relevant publicly available datasets that challenge the creation of new risk assessment models for semi- and fully automated vehicles. Therefore, this paper conducts a gap analysis to identify possible approaches using existing datasets and, additionally, a set of relevant vehicular data fields that could be incorporated in future data collection campaigns to address the challenge.

scholarly journals Fluid-based field representation for collision risk assessment on the road scenes

2019 ◽  
Author(s):  
Xuanpeng Li ◽  
Lifeng Zhu
Keyword(s):  
Risk Assessment ◽  
State Space ◽  
Intelligent Agents ◽  
Autonomous Vehicles ◽  
Dynamic Environment ◽  
Driving Safety ◽  
Collision Risk ◽  
Field Representation ◽  
On The Road ◽  
Short Term Prediction

Prediction of the likely evolution in the traffic scenes is a challenging task because of high uncertainty of sensing technology and dynamic environment. It leads to failure of planning for intelligent agents like autonomous vehicles. In this paper, we propose a fluid-based physical model to present the influence of surrounding object's motion on driving safety. In our pipeline, the input sensor could be LiDAR, camera, or multi-modal data. We use a Kalman filter to estimate the state space of each detected object, and adopt the properties of stable fluid to build a riskmap based on the density field. The noisy state space are then modeled as the boundary conditions in the simulation of advection and diffusion process. We test our approach on the public KITTI dataset and find this model could handle the short-term prediction in case of misdetection and tracking failure caused by object occlusion, which shows promising in collision risk assessment on road scenes.

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