Accurate velocity estimation is key to vehicle control. While the literature describes how model-based and learning-based observers are able to estimate a vehicle’s velocity in normal driving conditions, the challenge remains to estimate the velocity in near-limits maneuvers while using only conventional in-car sensors. In this paper, we introduce a novel neural network architecture based on Long Short-Term Memory (LSTM) networks to accurately estimate the vehicle’s velocity in different driving conditions, including maneuvers at the limits of handling. The approach has been tested on real vehicle data and it provides more accurate estimations than state-of-the-art model-based and learning-based methods, for both regular and near-limits driving scenarios. Our approach is robust since the performance of the state-of-the-art observers deteriorates with higher dynamics, while our method adapts to different maneuvers, providing accurate estimations even at the vehicle’s limits of handling.

This paper presents the architecture of a high- density parking solution based on car-like robots specifically designed to move cars. The main difficulty is to park the vehicles close to one another which implies hard constraints on the robot motion and localization. In particular, this paper focuses on navigation in narrow lanes. We propose a Lyapunov- based control strategy that has been derived after expressing the problem in a Configuration Space formulation. The current solution has been implemented and tested on Stanley Robotics' robots and has been running in production for several months. Thanks to the Configuration Space formulation, we are able to guarantee the obstacles’ integrity. Moreover, a method for calibrating the GPS orientation with a high-precision is derived from the present control strategy.

In normal on-road situations, autonomous vehicles will be expected to have smooth trajectories with relatively little demand on the vehicle dynamics to ensure passenger comfort and driving safety. However, the occurrence of unexpected events may require vehicles to perform aggressive maneuvers, near the limits of their dynamic capacities. In order to ensure the occupant's safety in these situations, the ability to plan controllable but near-limits trajectories will be of very high importance. One of the main issues in planning aggressive maneuvers lies in the high complexity of the vehicle dynamics near the handling limits, which effectively makes state-of-the-art methods such as Model Predictive Control difficult to use. This article studies a highly precise model of the vehicle body to derive a simpler, constrained second-order integrator dynamic model which remains precise even near the handling limits of the vehicle. Preliminary simulation results indicate that our model provides better accuracy without increasing computation time compared to a more classical kinematic bicycle model. The proposed model can find applications for contingency planning, which may require aggressive maneuvers, or for trajectory planning at high speed, for instance in racing applications.

Most autonomous driving architectures separate planning and control phases in different layers, even though both problems are intrinsically related. Due to limitations on the available computational power, their levels of abstraction and modeling differ; in particular, vehicle dynamics are often highly simplified at the planning phase, which may lead to inconsistency between the two layers. In this paper, we study the kinematic bicycle model, which is often used for trajectory planning, and compare its results to a 9 degrees of freedom model. Modeling errors and limitations of the kinematic bicycle model are highlighted. Lastly, this paper proposes a simple and efficient consistency criterion in order to validate the use of this model for planning purposes.