ShapeFuture will drive innovation in fundamental Electronic Components and Systems (ECS) that are essential for robust, powerful, fail-operational and integrated perception, cognition, AI-enabled decision making, resilient automation and computing, as well as communications, for highly automated vehicles. The overarching vision of ShapeFuture is to bring ECS Innovation to the heart of Europe’s Mobility Transformation, thereby elevating sovereignty by perfecting programmable ECS solutions for intelligent, safe, connected, and highly automated vehicles. In this paper, we detail not only the vision and mission of the ShapeFuture project, but we also showcase the results achieved during the first year.

Autonomous vehicles represents a ground-breaking transportation technology which has the potential to provide many benefits to the society. To fully utilize this technology, it is essential that the autonomous vehicle maintains safe behavior under different conditions and in case of failures. Fail-degraded strategies allow the vehicle to react safely to these situations, while maintaining to some extent the vehicle’s autonomous functionality. However, defining a framework for scalable fail-degraded systems, given the system’s complexity, remains to be a challenge. This paper focuses on identifying concepts and tools that enables scalable fail-degraded behavior for autonomous vehicles. The article includes a taxonomy to clarify holistic monitoring and representation concepts. Based on these concepts, scalable monitoring techniques are identified and classified. Afterwards, safety reasoning frameworks and adaptation mechanisms for fail-degraded autonomous vehicles are discussed. According to the discussed literature, several research gaps are identified.

Autonomous vehicles today are highly dependent on high-definition maps of the area they navigate in. This creates economic barriers in the way of the massive deployment of this technology, which can be overcome by estimating the drivable area onboard the autonomous vehicle. This task has been exhaustively explored using RGB cameras while LiDAR-only approaches are less common. In this paper, we propose LOGIC: a LiDAR-Only Geometric-Intensity Channel-based method for drivable area estimation. Our approach first obtains proposals of the drivable area by leveraging different pointcloud analysis procedures, including geometrical features, intensity evaluation and relations between neighbouring points. Then, these proposals are modeled as probabilistic drivability estimations and fused over time on a grid. This way of proceeding allows for a comprehensive analysis of the LiDAR data while also producing robust estimations. In addition, grid-level fusion enables the potential accommodation of additional navigable area detection methods or sensor inputs. Our method is able to match the performance of state-of-the-art methods without training or case-by-case parameter tuning while being tested on over 37000 LiDAR frames in the Waymo Open Dataset [1].

Automated vehicles are expected to enter the market in the coming years. To achieve this goal, it is essential to develop vehicles that prioritize both safety and reliability, but they should also ensure a comfortable user experience. As a result, autonomous driving functions should adapt to the individual preferences and requirements of drivers. This paper proposes an algorithm to dynamically update the parameters of a motion planner while driving to fit a driving style based on human-driver data. The motion planner generates multiple trajectory candidates and it can adjust the selection criteria online to prioritize the trajectory that matches better to a given behavior. The system was implemented in a real vehicle and compared to different human drivers, showing that it can reproduce their driving styles.

Motion prediction is a key factor towards the full deployment of autonomous vehicles. It is fundamental in order to ensure safety while navigating through highly interactive and complex scenarios. Lack of visibility due to an obstructed view or sensor range poses a great safety issue for autonomous vehicles. The inclusion of occlusion in interaction-aware approaches is not very well explored in the literature. In this work, the MultIAMP framework, which produces multimodal probabilistic outputs from the integration of a Dynamic Bayesian Network and Markov chains, is extended to tackle occlusions. The framework is evaluated with a state-of-the-art motion planner in two realistic use cases.

Motion prediction is a key factor towards the full deployment of autonomous vehicles. It is fundamental in order to assure safety while navigating through highly interactive complex scenarios. In this work, the framework IAMP (Interaction-Aware Motion Prediction), producing multi-modal probabilistic outputs from the integration of a Dynamic Bayesian Network and Markov Chains, is extended with a learning-based approach. The integration of a machine learning model tackles the limitations of the ruled-based mechanism since it can better adapt to different driving styles and driving situations. The method here introduced generates context-dependent acceleration distributions used in a Markov-chain-based motion prediction. This hybrid approach results in better evaluation metrics when compared with the baseline in the four highly-interactive scenarios obtained from publicly available datasets.

Dynamic occupancy grids (DOGs) have raised interest in the last years due to their ability to fuse information without explicit data association, to represent free space and arbitrary-shape objects and to estimate obstacles’ dynamics. Different works have presented strategies with demonstrated good performance. Most of them rely on LiDAR sensors, and some have shown that including additional velocity measurements enhance the estimation. This work aims at showing that velocity information can be directly inferred from objects displacement. Thus, a strategy using velocity feedback and its inclusion in the DOG is presented. The qualitative and quantitative analysis of results obtained from real data experimentation show a very good performance, specially in dynamic changing situations.

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.

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3.

To safely navigate in complex scenarios is crucial to know the predictions of the vehicles involved in the scene.The future behavior of the traffic participants is dependent on their intentions, the road layout and the interaction between them.In this work, a framework is presented to compute the motion predictions of the surrounding vehicles considering all possible routes obtained from a given map.At each time step, with a Dynamic Bayesian Network, the probability of being on a specific route and the intention to change lanes are computed.Our framework, based on Markov chains, is generic and can handle various road layouts and any number of vehicles.We apply the framework in a two-lane highway and evaluate the influence of different lane-changing methods on the predictions of the vehicles present at the scene.

A number of urban driving situations are still today too challenging to be handled by an autonomous driving system (ADS), and an intervention from humans inside the vehicle may be necessary. In this work, a novel traded control architecture is proposed to enhance the operational domain of the ADS under the premise that vehicles and humans may need to adapt their cooperation level depending on the context. To that end, a complexity level will be defined and computed in real time for each driving scene, and the role of the ADS and the human operator will be defined accordingly. With this information in hand, the system will alert the human operator when the involvement level will be lower than required or when a complex scene is detected.

Autonomous driving has the potential to disruptively change the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI controlled vehicle demonstrators) achieved until year 2.

The complexity to handle complex situations in automated driving requires increasing computational resources. In this work, we propose a machine learning approach for motion planning aiming at optimizing the set of path candidates to be evaluated in accordance with the driving context. Thus, the computation cost of the whole motion planning strategy can be reduced while generating safe and comfortable trajectories when required. The proposed strategy has been implemented in a real experimental platform and validated in different operating environments, successfully providing high quality trajectories in a small time frame.

Autonomous vehicles will find an infinite number of possible scenarios while driving in urban environments and need to react in a proper manner. For that reason, it is important to have algorithms that can propose driving alternatives for different type of scenarios in a global and unified way instead of using rule-based algorithms which depend on the driving scene. This paper presents a reachability estimation algorithm designed to obtain a safe and comfort-optimized trajectory set for different driving scenarios. First, a finite number of path candidates are created using Bézler curves. Then, all valid path candidates are combined with the reachable sets of dynamic obstacles to generate speed profiles consistent with safety and comfort requirements. The output of this algorithm would allow a decision-making strategy to select the optimum candidate depending on different criteria.

Among the actual trends that will affect society in the coming years, autonomous driving stands out as having the potential to disruptively change the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches also due to missing reliable environment perception and sensor fusion. PRYSTINE will realize Fail-operational Urban Surround perceptION (FUSION) which is based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. In this paper, we detail the vision of the PRYSTINE project and we showcase the results achieved during the first year.

Autonomous driving requires general methods to generalize unpredictable situations and reason in complex scenarios where safety is critical and the vehicle must react in a reliable manner. In this sense, digital maps are a crucial component for relating the location of the vehicle and identifying the different road features. In this work, we present a decision-making architecture which does not require detailed prior maps. Instead, OSM is used to plan a global route and an automatically generate driving corridors, which are adapted using a proposed vision-based algorithm. Moreover, a grid-based approach is also applied to consider the localization uncertainty. Those self-generated driving corridors are used by the local planner to plan the trajectories the vehicle will follow. Our approach integrates global, local and HMI components to provide the required functionalities for autonomous driving in a general manner.

The accurate classification of moving object in urban traffic scenarios is a key element for safe decision-making of intelligent vehicles. Multi-sensor approaches are typically based on features or specific objects models, which results in either an intrinsic lack of robustness or a significant design complexity. This paper proposes to take advantage of a Bayesian occupancy framework for perception, introducing a classifier combining grid-based footprints and speed estimations. The preliminary results obtained with a Lidar under a very realistic simulation framework are very promising.

Safe and human-like path planning in urban environments remain one of the most challenging problems for autonomous driving. This paper proposes a solution to plan traffic-free optimal and smooth paths using the most spread open source navigation data structure, OpenStreetMaps. To that end, a procedure to automatically transform raw OSM data into the drivable space is combined with a MINLP optimization algorithm that finds the most suitable position of intermediate waypoints to connect Bézier primitive curves. The proposed planner is validated in a real complex roundabout, for which very smooth paths are generated, even considering obstacles.

Nowadays, some developments in the vehicle industry permit a safe and comfortable driving. However, several manufactures and research groups are still working in the improvement of the control strategies and path smoothing algorithms. In this paper, a new trajectory generation approach for autonomous vehicles in urban scenarios, considering parametric equations, is proposed. An algorithm that considers Bezier curves and circumference parametric equations for a real vehicle, specifically in roundabout and urban intersections is presented. This approach is generated in real time and can be adapted to dynamic changes in the route. A smooth trajectory generator computationally efficient and easily implementable is proposed. Moreover, this new trajectory generator reduces the control actions, generated with to a fuzzy controller. Some trials have been performed in an urban circuit with promising performance.

We study the problem of automatic configuration of the initial position, the so-called waypoint, from which to initiate a robust and accurate docking maneuver using nonholonomic (car-like) robotic forklifts in the context of automated manufacturing. The proper selection of these positions is of paramount importance to operate with the industrial grade of accuracy, repeatability, and reliability required by load transfer operations in industrial settings. An unconstrained optimization method coupled with probabilistic techniques is proposed to solve this problem. The proposed method permits to increase significantly the flexibility and adaptability of the autonomous robotic forklifts.

This paper describes the development of a filter algorithm based on the behaviour of biological swarms. The main goal of the algorithm is to perform vehicle localization by combining the data from different sensors - GPS, IMU, speedometers, etc. - and digital maps. In this sense, the algorithm considers several solutions at the same time like Particles Filters. The algorithm has been developed off-line using real data captured from an instrumented vehicle at LIVIC. Performance of the algorithm has been validated and compared with and EKF with encouraging results.

Autonomous navigation is one of the most important challenges in the outdoor mobile robot field. For an automatic vehicle (which can be considered a type of outdoor mobile robot), path following can be implemented using global positioning systems (GPS) to allow the configuration of different navigation styles such as the shortest or fastest route, toll avoidance, etc., and even the definition of new routes. The main problem is when an unexpected circumstance occurs - traffic accident, road closure, etc. This paper presents an autonomous vehicle guidance system based on fuzzy logic systems to resolve unexpected road situations. A fuzzy steering controller performs the autonomous navigation, allowing reverse as well as forward driving in urban environments. Good performance was obtained in trials performed with a commercial electric Citroën Berlingo van on a private driving circuit.

Vehicles equipped with intelligent systems designed to prevent accidents, such as collision warning systems (CWSs) or lane-keeping assistance (LKA), are now on the market. The next step in reducing road accidents is to coordinate such vehicles in advance not only to avoid collisions but to improve traffic flow as well. To this end, vehicle-to-infrastructure (V2I) communications are essential to properly manage traffic situations. This paper describes the AUTOPIA approach toward an intelligent traffic management system based on V2I communications. A fuzzy-based control algorithm that takes into account each vehicle's safe and comfortable distance and speed adjustment for collision avoidance and better traffic flow has been developed. The proposed solution was validated by an IEEE-802.11p-based communications study. The entire system showed good performance in testing in real-world scenarios, first by computer simulation and then with real vehicles.

In this work, a solution for clustering and tracking obstacles in the area covered by a LIDAR sensor is presented. It is based on a combination of simple artificial intelligence techniques and it is conceived as an initial version of a detection and tracking system for objects of any shape that an autonomous vehicle might find in its surroundings. The proposed solution divides the problem into three consecutive phases: 1) segmentation, 2) fragmentation detection and clustering and 3) tracking. The work done has been tested with real world LIDAR scan samples taken from an instrumented vehicle.

Traffic merging in urban environments is one of the main causes of traffic congestion. From the driver's point of view, the difficulty arises along the on-ramp where the merging vehicle's driver has to discern whether he should accelerate or decelerate to enter the main road. In parallel, the drivers of the vehicles already on the major road may have to modify their speeds to permit the entrance of the merging vehicle, thus affecting the traffic flow. This paper presents an approach to merging from a minor to a major road in congested traffic situations. An automated merging system that was developed with two principal goals, i.e., to permit the merging vehicle to sufficiently fluidly enter the major road to avoid congestion on the minor road and to modify the speed of the vehicles already on the main road to minimize the effect on that already congested main road, is described. A fuzzy controller is developed to act on the vehicles' longitudinal control - throttle and brake pedals - following the references set by a decision algorithm. Data from other vehicles are acquired using wireless vehicle-to-infrastructure (V2I) communication. A system installed in the infrastructure that is capable of assessing road traffic conditions in real time is responsible for transmitting the data of the vehicles in the surrounding area. Three production vehicles were used in the experimental phase to validate the proposed system at the facilities of the Centro de Automática y Robótica with encouraging results.