Autonomous Intersection Crossing With Vehicle Location Uncertainty

Abstract

To date, a large body of the literature has looked into the problem of autonomous intersection crossings facilitated by Connected Autonomous Vehicles (CAVs). Nevertheless, existing approaches assume that CAVs know their exact location and system state. This work presents a novel framework that allows for an optimized intersection management, which considers vehicle location uncertainties for linear-Gaussian systems. Building upon the proposed framework, a family of 0 − 1 integer linear programming optimizations are presented that can set, sequentially or simultaneously, the acceleration profiles of all vehicles in the intersection. Extensive simulation results are presented, proving that the proposed framework represents a real-time nearoptimal approach that maximizes intersection throughput with probabilistic collision avoidance guarantees.

Optimizing Vehicle Re-Ordering Events in Coordinated Autonomous Intersection Crossings Under CAVs' Location Uncertainty

“…Finally, in a distributed approach, in the case of a mixed-traffic highway scenario, a Monte-Carlo Tree Search is used [29] to account for human-driven maneuvering uncertainty. Contrary to the aforementioned studies, only our work in [9]- [11] focuses on improving capacity and safety in autonomous intersection crossings, while considering location uncertainty due to sensor and prediction errors.…”

“…Such update system state estimates bear less uncertainty and propagate a smaller error to future time instances compared to the ones computed at the entrance to the area managed by the IM, hence unlocking better performance without compromising the CAVs' safety. Finally, as shown in AVOID-ORDER and AVOID-ORDER EVENT, introducing smart event-triggering techniques heavily reduces the computational and communication complexity of periodic re-optimizations, even though a high percentage of the performance gains on our previous work [9], [10] can be retained.…”

“…Several approaches also model the uncertainty of human-driven vehicles at intersections, e.g., [8], for planning the trajectory of autonomous vehicles. The only framework that accounts for uncertainty in CAVs trajectories when optimizing intersections' performance is our previous work in [9]. However, in that work CAVs' trajectories are optimized only once, at the entrance to the area around the intersection.…”

“…However, in that work CAVs' trajectories are optimized only once, at the entrance to the area around the intersection. Our work in [10], [11] extends [9], accounting for demand management, i.e., selecting the best moment for a CAV to enter the intersection, and proposing a preliminary study on how communication may help control re-optimizations. Building on our previous efforts, this work considers an adaptive scheme, where an intersection manager (IM) collects the CAVs' states and associated uncertainties and, with the obtained holistic view of the area, decides the CAVs' future controls for crossing an intersection.…”

“…• building on our uncertainty-aware mathematical framework in [9] and exploiting information communicated from CAVs as in [11], a periodic control optimization for autonomous intersection management is proposed, i.e., the AVOID-PERIOD approach; • exploiting also the optimization of CAVs' intersection crossing order, a novel framework maximizing intersection capacity is also proposed, i.e., the AVOID-ORDER approach. A toy example is also used to showcase how, even if some CAVs may be penalized, re-ordering is critical for the system-wide performance; • the AVOID-EVENT and AVOID-ORDER EVENT approaches are also developed, in order to extend the corresponding periodic optimizations by exploiting eventtriggering.…”