English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a Stochastic MPC (SMPC) formulation for path planning withautonomous vehicles in scenarios involving multiple agents with multi-modalpredictions. The multi-modal predictions capture the uncertainty of urbandriving in distinct modes/maneuvers (e.g., yield, keep speed) and drivingtrajectories (e.g., speed, turning radius), which are incorporated formulti-modal collision avoidance chance constraints for path planning. In thepresence of multi-modal uncertainties, it is challenging to reliably computefeasible path planning solutions at real-time frequencies ($\geq$ 10 Hz). Ourmain technological contribution is a convex SMPC formulation thatsimultaneously (1) optimizes over parameterized feedback policies and (2)allocates risk levels for each mode of the prediction. The use of feedbackpolicies and risk allocation enhances the feasibility and performance of theSMPC formulation against multi-modal predictions with large uncertainty. Weevaluate our approach via simulations and road experiments with a full-scalevehicle interacting in closed-loop with virtual vehicles. We consider distinct,multi-modal driving scenarios: 1) Negotiating a traffic light and a fast,tailgating agent, 2) Executing an unprotected left turn at a trafficintersection, and 3) Changing lanes in the presence of multiple agents. For allof these scenarios, our approach reliably computes multi-modal solutions to thepath-planning problem at real-time frequencies.

Predictive Control for Autonomous Driving with Uncertain, Multi-modal Predictions