Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures
This paper presents fast non-sampling based methods to assess the risk for trajectories of
autonomous vehicles when probabilistic predictions of other agents' futures are generated …
autonomous vehicles when probabilistic predictions of other agents' futures are generated …
Fast risk assessment for autonomous vehicles using learned models of agent futures
This paper presents fast non-sampling based methods to assess the risk of trajectories for
autonomous vehicles when probabilistic predictions of other agents' futures are generated …
autonomous vehicles when probabilistic predictions of other agents' futures are generated …
Non-gaussian chance-constrained trajectory planning for autonomous vehicles under agent uncertainty
Agent behavior is arguably the greatest source of uncertainty in trajectory planning for
autonomous vehicles. This problem has motivated significant amounts of work in the …
autonomous vehicles. This problem has motivated significant amounts of work in the …
Probabilistic trajectory prediction for autonomous vehicles with attentive recurrent neural process
Predicting surrounding vehicle behaviors are critical to autonomous vehicles when
negotiating in multi-vehicle interaction scenarios. Most existing approaches require tedious …
negotiating in multi-vehicle interaction scenarios. Most existing approaches require tedious …
Conditional generative neural system for probabilistic trajectory prediction
J Li, H Ma, M Tomizuka - 2019 IEEE/RSJ International …, 2019 - ieeexplore.ieee.org
Effective understanding of the environment and accurate trajectory prediction of surrounding
dynamic obstacles are critical for intelligent systems such as autonomous vehicles and …
dynamic obstacles are critical for intelligent systems such as autonomous vehicles and …
UQNet: Quantifying uncertainty in trajectory prediction by a non-parametric and generalizable approach
Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory
prediction contains many sources of uncertainty in data and modeling. A thorough …
prediction contains many sources of uncertainty in data and modeling. A thorough …
Heterogeneous-agent trajectory forecasting incorporating class uncertainty
Reasoning about the future behavior of other agents is critical to safe robot navigation. The
multiplicity of plausible futures is further amplified by the uncertainty inherent to agent state …
multiplicity of plausible futures is further amplified by the uncertainty inherent to agent state …
A risk-aware architecture for autonomous vehicle operation under uncertainty
A significant barrier to deploying autonomous vehicles (AVs) on a massive scale is safety
assurance. Several technical challenges arise due to the uncertain environment in which …
assurance. Several technical challenges arise due to the uncertain environment in which …
Deep kernel learning for uncertainty estimation in multiple trajectory prediction networks
J Strohbeck, J Müller, M Herrmann… - 2022 IEEE/RSJ …, 2022 - ieeexplore.ieee.org
Predicting future paths of vehicles or pedestrians is an essential task for automated vehicles
to allow for planning the own trajectory. Using predicted paths, a planning algorithm can, eg …
to allow for planning the own trajectory. Using predicted paths, a planning algorithm can, eg …
[PDF][PDF] Trajflow: Learning the distribution over trajectories
A Mészáros, J Alonso-Mora… - arXiv preprint …, 2023 - motionpredictionicra2023.github.io
Predicting the future behaviour of people remains an open challenge for the development of
risk-aware autonomous vehicles. An important aspect of this challenge is effectively …
risk-aware autonomous vehicles. An important aspect of this challenge is effectively …