Localization within high definition maps is a key problem for autonomous navigation as vehicles need to extract information from them. In addition, many navigation tasks are defined with respect to map features. For instance, estimating the cross-track and along-track gaps of a vehicle with respect to a given path is critical for lane keeping or intersection management. Map-based localization is also important for cooperative tasks like platooning in curved roads or lane changing. This work studies different methods to compute map-based coordinates defined as curvilinear abscissa, lateral distance and heading with respect to paths in high definition maps. Four approaches using polylines, lanelets and splines are compared. Thanks to real experiments, the discontinuity issues of polylines used in current high definition maps are evaluated and we discuss advantages and drawbacks of splines-based and lanelet methods. We also report experimental results corresponding to a platoon of two vehicles in curved roads and evaluate the effects of the use of low cost GNSS receivers.

Despite the tremendous growth of research regarding fully autonomous vehicles in the past few years, many safety critical scenarios, such as the crossing of roundabouts, are still open issues. One of the main challenge is to deal with the lack of visibility in complex environments. Vehicle-tovehicle and vehicle-to-infrastructure communications offer an appealing solution to handle these situations in a cooperative way. To avoid computing an ad hoc control strategy for every possible scenario, we propose in this paper to adapt the concept of virtual platooning to roundabout crossing. This idea allows a single platooning control law to handle complex scenarios such as intersection and roundabout crossings. This work combines the use of high definition maps and a curvilinear coordinates framework to deal with any kind of roundabouts. The proposed approach is not limited to communicating autonomous vehicles but can also be used with manually driven communicating vehicles or non-communicating vehicles with the help on the infrastructure. A formal proof of the correctness of this approach is given and simulations have been carried out with a high definition map of a real roundabout. We also introduce a novel graphical representation called safety diagram to study de performances of our approach.

Performing autonomous driving in urban environments is a challenging task, especially when there is a reduced visibility of traffic participants in complex driving scenarios. For this reason, we investigate the advantages of cooperative perception systems to enhance on-board perception capabilities. In this paper, we present a cooperative roadside vision system for augmenting the embedded perception of an autonomous vehicle navigating in a complex urban scenario. In particular, we use an HD map to implement a map-aided tracking system that merges the information from both onboard and remote sensors. The road users detected by the on-board LiDAR are represented as bounding polygons that include the localization uncertainty whereas, for the camera, the detected bounding boxes are projected in the map frame using a geometric constrained optimization. We report experimental results using two experimental vehicles and a roadside camera in a real traffic scenario in a roundabout. These results quantify how the cooperative data fusion extends the field of view and how the accuracy of the pose estimation of perceived objects is improved.

In the paper a fast and consistent method to manage uncertainties on detected traffic agents providing reliable results is presented. The information provided by a LiDARbased object detector is combined with a high-definition map to identify the drivable space of the carriageway. Because the use of a HD map requires the use of a localization system, the uncertainty of the estimated pose shall be handled carefully. A novel approach taking into account the localization uncertainty in the perception task by direct propagation of it onto the LiDAR points is proposed. It is compared with a classical propagation that relies on linearized approximation. The good performances of this approach in terms of integrity are demonstrated by the use of real data acquired at the entrance of a roundabout being a particularly complex situation.

Although autonomous vehicle technology has evolved significantly in recent years, the navigation of self-driving vehicles in complex scenarios is still an open issue. One of the major challenges in these conditions is safe navigation on roads open to public traffic. The main issue is the interaction of the autonomous vehicle with regular traffic, as the behaviors and intentions of human-driven vehicles are hard to predict and understand. In this paper we propose a strategy to allow an autonomous vehicle to safely cross a multi-lane roundabout. Our approach uses a High-Definition (HD) map to predict at lane level the future situation, harnessing the concept of virtual instances of road users, which is a key concept in anticipating the situation in a roundabout that can be represented by a navigation graph with loops. This paper presents a methodology that uses intervals representing road occupancy by vehicles, with the road being widened to reflect uncertainties in localization. Our method safely avoids collisions and guarantees that no priority constraints are violated during the insertion maneuver. Moreover, the method does not provide an overly cautious insertion policy, i.e., an autonomous vehicle does not wait for a long time before the insertion. The performance of our strategy was evaluated using the SUMO simulation framework. To better evaluate the complexity of the simulation scenario, a highly interactive vehicle flow was generated using real dynamic traffic data from the INTERACTION dataset. We report real tests carried out with an experimental self-driving vehicle on a test circuit. Our results show that this approach is easy to integrate into an embedded system and that it allows roundabouts to be crossed with a high level of safety.