This paper presents a global route planning algorithm designed as part of the navigation module embedded in an Autonomous Driving System (ADS). Unlike trajectory planners, which focus on local maneuvering and vehicle control, this algorithm determines optimal routes at a higher level, prioritizing dynamic adaptation to traffic conditions and regulatory elements. The planner aims to minimize travel time rather than merely reducing the total distance traveled, making it particularly effective in urban environments where traffic signals, vehicle interactions, and road regulations significantly impact journey duration. To achieve this, the algorithm dynamically adjusts to real-time variations in traffic flow and control measures. Additionally, it integrates risk-aware routing by imposing penalties on roads with higher pedestrian interaction, enhancing safety and increasing public acceptance of ADS technology. Designed for efficiency and scalability, the algorithm is lightweight enough to run on microcontroller-based embedded systems, ensuring feasibility for real-world deployment in constrained computing environments. The algorithm was tested using a Renault mass-production car, demonstrating its applicability in real-world driving scenarios.

Abstract Autonomous vehicles in logistics and industrial environments demand robust and efficient perception systems. This study presents a LiDAR-based perception system designed for such environments, focusing on real-time deterministic obstacle detection and tracking with limited computational power. The proposed multi-stage approach leverages 3D data from LiDAR sensors. First, ground removal is performed to filter out static ground points. Then, a filtering step is applied using precomputed maps of the navigation area to filter out static zones from the LiDAR point clouds. After, object segmentation distinguishes structural elements from potential obstacles, followed by clustering and Principal Component Analysis (PCA) to accurately estimate obstacle pose and volume. An obstacle-tracking method ensures continuous monitoring over time. Extensive experiments in realistic logistics and industrial scenarios have been performed, comparing the proposed approach to state-of-the-art deep-learning-based methods, demonstrating the system’s high performance in both accuracy and efficiency.

The rapid advancements in autonomous vehicle software present both opportunities and challenges, especially in enhancing road safety. The primary objective of autonomous vehicles is to reduce accident rates through improved safety measures. However, the integration of new algorithms into the autonomous vehicle, such as artificial intelligence methods, raises concerns about the compliance with established safety regulations. This paper introduces a novel supervisor architecture based on behavior trees, aligned with established standards and designed to supervise vehicle functional safety in real time. It specifically addresses the integration of algorithms into industrial road vehicles, adhering to the ISO 26262. The proposed supervisor architecture involves the detection of hazards and compliance with functional and technical safety requirements when a hazard arises. This methodology, implemented in this study in a Renault Mégane (currently with SAE level 3 of automation capabilities), not only guarantees compliance with safety standards, but also paves the way for safer and more reliable autonomous driving technologies.

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.

This paper presents the design and implementation of an intelligent switched control for lateral control of autonomous vehicles. The switched control is designed based on Linear Parameter-Varying (LPV) and Youla-Kucera (YK) parameterization. The proposed intelligent system aims to optimize the control switching performance using a Reinforcement Learning (RL) model. The presented approach studies the critical problem of initial or sudden large lateral errors in lane-tracking or lane-changing. It ensures stable and smooth switching performance to provide a smooth vehicle response regardless of the lateral error. The proposed RL-based switching strategy is validated using a RENAULT simulator on MATLAB, and compared to another modeled switching strategy with encouraging results.

This paper presents the design and experimental validation of grid-based and Linear Fractional Transformation (LFT) approaches for the lateral control of autonomous vehicles. These new methodological approaches are compared together with the classical polytopic approach from the theoretical design to the real implementation on a real automated Renault ZOE vehicle. A solution is proposed to deal with both lane change and lane tracking problems, using a single LPV controller, by adapting the look-ahead distance. Each LPV controller is designed based on LPV/$\mathcal {H}_\infty $concept. Performance comparison includes computational costs, vehicle performance (i.e. lateral tracking error or control effort optimization) and on-board integration complexity. Simulation and experimental results on a private test track are included to support main findings.

This paper proposes a multi-model adaptive control (MMAC) algorithm based on Youla-Kucera (YK) theory to deal with heterogeneity in cooperative adaptive cruise control (CACC) systems. The main idea of MMAC is to choose the plant in a predefined set that best approximates the system dynamics, applying the corresponding predesigned controller. A set of linear plants describing different vehicle dynamics is defined. Different CACC controllers are designed depending on these linear plants. Simulation and experimental results prove how MMAC determines the closest plant in the set, choosing the CACC system able to ensure string stability.

Urban environments are among the most challenging scenarios for car-following systems, since pedestrians may interfere with the platoon unexpectedly. To address this problem, this paper proposes a cooperative system using vehicle-to-vehicle and vehicle-to-pedestrian communication links. A fractional-order control-based cooperative adaptive cruise control benefits of communication for tighter inter-vehicle distances, while pedestrian communication is fused with LiDAR sensing to allow the detection of occluded pedestrians. The prediction of the pedestrians' trajectories is used to perform a speed reduction or an emergency braking that interrupts the car-following yif necessary. Whenever a platoon decoupling occurs, a gap-closing maneuver is executed so that the ego-vehicle rejoins the platoon in a string stable way. The complete system was tested on experimental platforms at inria facilities, providing encouraging results and demonstrating the correct performance of the integrated systems.

Nowadays, the development of Intelligent Transportation System (ITS) is increasing due to its versatility, adaptability and use of clean energy. There are a number of pass and on-going projects worldwide dealing with the different challenges and approaches to solve road transport-related issues. Some of them are dealing with the Cybernetic Transportation Systems (CTS), which is an urban mobility concept based on the automation of door-to-door transport systems i.e. the Cybercars as a two-passenger CTS. This paper presents the functional architecture of the CTSs and the development of an automated global planner. Specifically, a new approach that considers the automatization of the global planner stage, which allows path calculations and modifications in real time, considering on-demand stopping points. The experimental tests show a proper behaviour in our facilities at INRIA-Rocquencourt (France).

This paper examines the already commercially available Adaptive Cruise Controller (ACC) system, and its evolution by adding vehicle-to-vehicle communications: the cooperative ACC (CACC) version. The transition between ACC and CACC controllers will be done through the new control technique called Plug&Play. This technique is able to deal with living systems and the changes in its sensors and actuators to preserve the system stable. The aim is to ensure the system stability during transitions between controllers when the vehicle-to-vehicle communication link is changing from unavailable to available or vice versa.

Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

Power consumption and battery life are two of the key aspect when it comes to improve electric transportation systems autonomy. This paper describes the design, development and implementation of a speed profile generation based on the calculation of the optimal energy consumption for electric Cybercar vehicles for each of the stretches that are covering. The proposed system considers a commuter daily route that is already known. It divides the pre-defined route into segments according to the road slope and stretch length, generating the proper speed reference. The developed system was tested on an experimental electric platform at Inria's facilities, showing a significant improvement in terms of energy consumption for a pre-defined route.

Intelligent vehicle cooperation based on reliable communication systems contributes not only to reducing traffic accidents but also to improving traffic flow. Adaptive cruise control (ACC) systems can gain enhanced performance by adding vehicle-vehicle wireless communication to provide additional information to augment range sensor data, leading to cooperative ACC (CACC). This paper presents the design, development, implementation, and testing of a CACC system. It consists of two controllers, one to manage the approaching maneuver to the leading vehicle and the other to regulate car-following once the vehicle joins the platoon. The system has been implemented on four production Infiniti M56s vehicles, and this paper details the results of experiments to validate the performance of the controller and its improvements with respect to the commercially available ACC system.

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.

This work presents a driving system designed for virtual racing situations. It is based on a complete modular architecture capable of automatically driving a car along a track with or without opponents. The architecture is composed of intuitive modules, with each one being responsible for a basic aspect of car driving. Moreover, this modularity of the architecture will allow us to replace or add modules in the future as a way to enhance particular features of particular situations. In the present work, some of the modules are implemented by means of hand-designed driving heuristics, whereas modules responsible for adapting the speed and direction of the vehicle to the track's shape, both critical aspects of driving a vehicle, are optimized by means of a genetic algorithm that evaluates the performance of the controller in four different tracks to obtain the best controller in a large number of situations; the algorithm also penalizes controllers that go out of the track, lose control, or get damaged. The evaluation of the performance is done in two ways. First, in runs with and without adversaries over several tracks. And second, the architecture was submitted as a participant to the 2010 Simulated Car Racing Competition, which in end won laurels. © 2012 Wiley Periodicals, Inc.

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.

Collision avoidance is one of the most difficult and challenging automatic driving operations in the domain of intelligent vehicles. In emergency situations, human drivers are more likely to brake than to steer, although the optimal maneuver would, more frequently, be steering alone. This statement suggests the use of automatic steering as a promising solution to avoid accidents in the future. The objective of this paper is to provide a collision avoidance system (CAS) for autonomous vehicles, focusing on pedestrian collision avoidance. The detection component involves a stereo-vision-based pedestrian detection system that provides suitable measurements of the time to collision. The collision avoidance maneuver is performed using fuzzy controllers for the actuators that mimic human behavior and reactions, along with a high-precision Global Positioning System (GPS), which provides the information needed for the autonomous navigation. The proposed system is evaluated in two steps. First, drivers' behavior and sensor accuracy are studied in experiments carried out by manual driving. This study will be used to define the parameters of the second step, in which automatic pedestrian collision avoidance is carried out at speeds of up to 30 km/h. The performed field tests provided encouraging results and proved the viability of the proposed approach.

This paper describes the results of vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) experiments implementing cooperative maneuvering for three different vehicles driving automatically. The cars used were cybercars from the Institut National de Recherche en Informatique et Automatique (INRIA), (France), which are fully automated road vehicles, and two mass-produced cars-one a Smart Fortwo car from TNO (Netherlands) equipped with additional actuators and sensors and the other a convertible Citroën C3 from IAI (Spain) that uses sensorial information to manage the actuators. The cars communicate by a wireless mesh network over Wi-Fi using the optimized link state routing (OLSR) ad-hoc protocol. The entire communication task is embedded in a small MIPS Linux Box (4G System Cube) that is transparent for the cars. A standard framework was defined with the parameters needed to perform adaptive cruise control (ACC) and intersection maneuvers among the cars, as well as emergency stops via a signal sent by the infrastructure. The experiments were carried out in La Rochelle (France) during the final demonstration of the European Union (EU) Cybercars-2 Project.

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.

Research on intelligent transport systems (ITSs) is steadily leading to safer and more comfortable control for vehicles. Systems that permit longitudinal control have already been implemented in commercial vehicles, acting on throttle and brake. Nevertheless, lateral control applications are less common in the market. Since a too-sudden turn of the steering wheel can cause an accident in a few seconds, good speed and position control of the steering wheel is essential. We present here a new cascade control architecture based on fuzzy logic controllers that emulate a human driver's behavior. The control architecture was tested on a real vehicle at different vehicle speeds. The results showed the use of a straightforward and intuitive fuzzy controller to give good performance.

The control of nonlinear systems has been putting especial attention in the use of Artificial Intelligent techniques, where fuzzy logic presents one of the best alternatives due to the exploit of human knowledge. However, several fuzzy logic real-world applications use manual tuning (human expertise) to adjust control systems. On the other hand, in the Intelligent Transport Systems (ITS) field, the longitudinal control (throttle and brake management) is an important topic because external perturbations can generate uncomfortable accelerations as well as unnecessary fuel consumption. In this work, we utilize a neuro-fuzzy system to use human driving knowledge to tune and adjust the input-output parameters of a fuzzy if-then system. The neuro-fuzzy system considered in this work is ANFIS (Adaptive-Network-based Fuzzy Inference System). Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous manual tuned controller, mainly in comfort and efficient use of actuators.

A major research topic in intelligent transportation systems (ITSs) is the development of systems that will be capable of controlling the flow of vehicular traffic through crossroads, particularly in urban environments. This could significantly reduce traffic jams, since autonomous vehicles would be capable of calculating the optimal speed to maximize the number of cars driving through the intersection. We describe the use of vehicle-to-vehicle (V2V) communications to determine the position and speed of the vehicles in an environment around a crossroad. These data are used to estimate the intersection point, and a fuzzy controller then modifies the speed of the cars without right of way according to the speed of the car with right of way. Experimental tests conducted with two mass-produced cars on a real circuit at the facilities of the Instituto de Automa¿tica Industrial, Consejo Superior de Investigaciones Cienti¿ficas, Madrid, Spain, gave excellent results.

The techniques of soft computing are recognized as having a strong learning and cognition capability as well as good tolerance to uncertainty and imprecision. These properties allow them to be applied successfully to intelligent transportation systems (ITS), a broad range of diverse technologies that designed to answer many transportation problems. The unmanned control of the steering wheel is one of the most important challenges faced by researchers in this area. This paper presents a method of automatically adjusting a fuzzy controller to manage the steering wheel of a mass-produced vehicle. Information about the state of the car while a human driver is handling it is captured and used to search, via genetic algorithms, for the best fit of an appropriate fuzzy controller. Evaluation of the fuzzy controller will take into account its adjustment to the human driver's actions and the absence of abrupt changes in its control surface, so that not only is the route tracking good, but the drive is smooth and comfortable for the vehicle's occupants.