High-definition map with accurate lane-level information is crucial for autonomous driving, but the creation of these maps is a resource-intensive process. To this end, we present a cost-effective solution to create lane-level roadmaps using only the global navigation satellite system (GNSS) and a camera on customer vehicles. Our proposed solution utilizes a prior standard-definition (SD) map, GNSS measurements, visual odometry, and lane marking edge detection points, to simultaneously estimate the vehicle's 6D pose, its position within a SD map, and also the 3D geometry of traffic lines. This is achieved using a Bayesian simultaneous localization and multi-object tracking filter, where the estimation of traffic lines is formulated as a multiple extended object tracking problem, solved using a trajectory Poisson multi-Bernoulli mixture (TPMBM) filter. In TPMBM filtering, traffic lines are modeled using B-spline trajectories, and each trajectory is parameterized by a sequence of control points. The proposed solution has been evaluated using experimental data collected by a test vehicle driving on highway. Preliminary results show that the traffic line estimates, overlaid on the satellite image, generally align with the lane markings up to some lateral offsets.

A research team at Ben-Gurion University have created a simple projection system able to trick Tesla's Autopilot into seeing things that aren't actually there. Using commercially available drones and a cheap projector - the kind a person might use to watch television in an apartment of small home - the team projected a series of deceptive images onto the road. The images included false traffic lines, a false speed limit sign, and an image of Elon Musk himself, projected on the road as if her were an endangered pedestrian. The researchers collectively labeled all these different visual phenomena as'phantoms,' according to a report in ArsTechnica. While the Tesla they tested reacted to every phantom in some way, most of its responses were fairly mild.