Learning hierarchical behavior and motion planning for autonomous driving
… behavior planner, which makes tactical decisions, might not … traffic lights and the distance to
the stop line of intersection, … with multilane roads with various traffic scenarios. In DAgger [31]…
the stop line of intersection, … with multilane roads with various traffic scenarios. In DAgger [31]…
Driving tasks transfer using deep reinforcement learning for decision-making of autonomous vehicles in unsignalized intersection
… self-driving cars drive through the intersections with no traffic … Then, the driving scenarios
of the intersection situations are … interaction protocol for connected autonomous vehicles at a …
of the intersection situations are … interaction protocol for connected autonomous vehicles at a …
Leveraging multiagent learning for automated vehicles scheduling at nonsignalized intersections
… , removing traffic lights has been immensely taken into con… Such interaction mechanism does
not require all vehicles to … , we consider a four-way intersection scenario which consists of a …
not require all vehicles to … , we consider a four-way intersection scenario which consists of a …
Multi-modal fusion transformer for end-to-end autonomous driving
… scenarios in the recent CARLA [21] versions, eg vehicles … interaction between the traffic light
(yellow) and the vehicles (… attends to the vehicles and traffic lights at intersections, albeit at a …
(yellow) and the vehicles (… attends to the vehicles and traffic lights at intersections, albeit at a …
Reachability-based decision-making for autonomous driving: Theory and experiments
… only intersections with all-way stop, but other intersection … , and signalized intersections
require either traffic signal timing … 13, we present a scenario when the EV starts to brake and …
require either traffic signal timing … 13, we present a scenario when the EV starts to brake and …
Social interactions for autonomous driving: A review and perspectives
… : 1) What is social interaction in road traffic scenes? 2) How to … (eg, traffic lights and signs),
which may influence human drivers’ … arms at a signalized urban intersection usually do not …
which may influence human drivers’ … arms at a signalized urban intersection usually do not …
St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning
… temporal information into the framework as most scenarios are tasked with video sources. …
the information of traffic lights, which helps the SDV to launch or stop at intersections. …
the information of traffic lights, which helps the SDV to launch or stop at intersections. …
[HTML][HTML] Autonomous driving architectures: insights of machine learning and deep learning algorithms
MR Bachute, JM Subhedar - Machine Learning with Applications, 2021 - Elsevier
… fluctuating traffic situations in dense city areas with varied road … task of Autonomous Driving
is to have traffic sign recognition … a specific case of intersection, where a circular traffic flow is …
is to have traffic sign recognition … a specific case of intersection, where a circular traffic flow is …
SceML: A graphical modeling framework for scenario-based testing of autonomous vehicles
… The bike plans to cross the road, whereas the ego vehicle … these three levels with the help
of our intersection scenario: … both actors drive into the intersection but do not collide. For both …
of our intersection scenario: … both actors drive into the intersection but do not collide. For both …
nuplan: A closed-loop ml-based planning benchmark for autonomous vehicles
… We show different driving scenarios to emphasize the … or right turns, interaction with cyclists,
interaction with pedestrians at … parked vehicles, stop controlled intersections and driving in …
interaction with pedestrians at … parked vehicles, stop controlled intersections and driving in …