Autonomous vehicles must be able to react in a timely manner to typical and unpredictable situations in urban scenarios. In this connection, motion planning algorithms play a key role as they are responsible of ensuring driving safety and comfort while producing human-like trajectories in a wide range of driving scenarios. Typical approaches for motion planning focus on trajectory optimization by applying computation-intensive algorithms, rather than finding a balance between optimatily and computing time. However, for on-road automated driving at medium and high speeds, determinism is necessary at high sampling rates. This work presents a trajectory planning algorithm that is able to provide safe, human-like and comfortable trajectories by using cost-effective primitives evaluation based on quintic Bézier curves. The proposed method is able to consider the kinodynamic constrains of the vehicle while reactively handling dynamic real environments in real-time. The proposed motion planning strategy has been implemented in a real experimental platform and validated in different real operating environments, successfully overcoming typical urban traffic scenes where both static and dynamic objects are involved.

Localization plays an important role in autonomous driving since a high level of accuracy in vehicle localization is indispensable for a safe navigation. Most of the motion planning approaches in the literature assume negligible uncertainty in vehicle localization. However, the accuracy of localization systems can be low by design or even can drop depending on the environment in some cases. In these situations, the localization uncertainty can be taken into consideration in motion planning to increase the system reliability. Accordingly, this work presents two main contributions: (i) a probabilistic occupancy grid-based approach for localization uncertainty propagation, and (ii) a motion planning strategy that relies on such occupancy grid. Thus, the proposed motion planning solution for automated driving is able to generate safe human-like trajectories in real time while considering the localization uncertainty, ego-vehicle constrains and obstacles. In order to validate the proposed algorithms, several experiments have been conducted in a real environment.

Both path and speed planning play key roles in automated driving, specially when a certain comfort level is required. This paper proposes a human-like speed planning method for already defined paths while minimizing travel time. The proposed method is able to compute a time-optimal speed profile that meet the given constrains with regard to speed, acceleration and jerk. For this purpose, an initial acceleration-limited approach is introduced. This algorithm serves as a starting point for the subsequent jerk-limited speed planning. Moreover, fallback strategies are included to manage critical driving situations where initial or final conditions cannot be met. The proposed approach has been tested and validated in an experimental platform through extensive trials in real environments. Its performance has been evaluated both in terms of quality of the computed speed profiles and with respect the required computing time.

Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.

Distinguishing obstacles from ground is an essential step for common perception tasks such as object detection-and-tracking or occupancy grid maps. Typical approaches rely on plane fitting or local geometric features, but their performance is reduced in situations with sloped terrain or sparse data. Some works address these issues using Markov Random Fields and Belief Propagation, but these rely on local geometric features uniquely. This article presents a strategy for ground segmentation in LiDAR point clouds composed by two main steps: (i) First, an initial classification is performed dividing the points in small groups and analyzing geometric features between them. (ii) Then, this initial classification is used to model the surrounding ground height as a Markov Random Field, which is solved using the Loopy Belief Propagation algorithm. Points are finally classified comparing their height with the estimated ground height map. On one hand, using an initial estimation to model the Markov Random Field provides a better description of the scene than local geometric features commonly used alone. On the other hand, using a graph-based approach with message passing achieves better results than simpler filtering or enhancement techniques, since data propagation compensates sparse distributions of LiDAR point clouds. Experiments are conducted with two different sources of information: nuScenes’s public dataset and an autonomous vehicle prototype. The estimation results are analyzed with respect to other methods, showing a good performance in a variety of situations.