Projects per year
Abstract / Description of output
Abstract—The ability to predict the intentions and driving trajectories of other vehicles is a key problem for autonomous driving. We propose an integrated planning and prediction system which leverages the computational benefit of using a finite space of maneuvers, and extend the approach to planning and prediction of sequences of maneuvers via rational inverse planning to recognise the goals of other vehicles. Goal recognition informs a Monte Carlo Tree Search (MCTS) algorithm to plan optimal maneuvers for the ego vehicle. Our system constructs plans which are explainable by means of rationality. Evaluation in simulations of four urban driving scenarios demonstrate the system’s ability to robustly recognise the goals of other vehicles while generating near-optimal plans.
|Number of pages||6|
|Publication status||E-pub ahead of print - 13 Jul 2020|
|Event||Interaction and Decision-Making in Autonomous-Driving: A Virtual Workshop at RSS 2020 - |
Duration: 13 Jul 2020 → 13 Jul 2020
|Workshop||Interaction and Decision-Making in Autonomous-Driving|
|Abbreviated title||IDA 2020|
|Period||13/07/20 → 13/07/20|
FingerprintDive into the research topics of 'Autonomous Driving with Interpretable Goal Recognition and Monte Carlo Tree Search'. Together they form a unique fingerprint.
- 1 Finished
1/01/19 → 31/12/22