Robust trajectory planning of autonomous vehicles at intersections with communication impairments

Neha Chohan, Mohammad A. Nazari, Henk Wymeersch, Themistoklis Charalambous

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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In this paper, we consider the trajectory planning of an autonomous vehicle to cross an intersection within a given time interval. The vehicle communicates its sensor data to a central coordinator which then computes the trajectory for the given time horizon and sends it back to the vehicle. We consider a realistic scenario in which the communication links are unreliable, the evolution of the state has noise (e.g., due to the model simplification and environmental disturbances), and the observation is noisy (e.g., due to noisy sensing and/or delayed information). The intersection crossing is modeled as a chance constraint problem and the stochastic noise evolution is restricted by a terminal constraint. The communication impairments are modeled as packet drop probabilities and Kalman estimation techniques are used for predicting the states in the presence of state and observation noises. A robust sub-optimal solution is obtained using convex optimization methods which ensures that the intersection is crossed by the vehicle in the given time interval with very low chance of failure.

Original languageEnglish
Title of host publicationProceedings of the 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019
Number of pages8
ISBN (Electronic)9781728131511
Publication statusPublished - 1 Sep 2019
MoE publication typeA4 Article in a conference publication
EventAllerton Conference on Communication, Control, and Computing - Monticello, United States
Duration: 29 Sep 20152 Oct 2015
Conference number: 53


ConferenceAllerton Conference on Communication, Control, and Computing
Abbreviated titleAllerton
Country/TerritoryUnited States


  • Intersection crossing
  • Robust trajectory planning
  • Unreliable communications.


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