Vehicles In Urban Environments Research Articles

A model predictive control approach for highly automated vehicles in urban environments

AbstractIn this paper, a model predictive control (MPC) approach for the lateral and longitudinal control of a highly automated electric vehicle with all-wheel drive and dual-axis steering is presented. For the prediction of state trajectories a two-track vehicle model is used. The MPC problem for trajectory tracking is formulated by controlling the front and rear steering angle as well as the individual drive torques with respect to actuator and design constraints. Beside the steering angles, the MPC controller computes the individual drive torques to not only match the reference velocity but also to support the lateral dynamics of the vehicle using torque vectoring. The MPC problem is solved using ACADOS, a software package for efficiently solving optimal control problems. The effectiveness of the proposed MPC scheme is demonstrated via simulation.

VTGNet: A Vision-Based Trajectory Generation Network for Autonomous Vehicles in Urban Environments

Traditional methods for autonomous driving are implemented with many building blocks from perception, planning and control, making them difficult to generalize to varied scenarios due to complex assumptions and interdependencies. Recently, the end-to-end driving method has emerged, which performs well and generalizes to new environments by directly learning from export-provided data. However, many existing methods on this topic neglect to check the confidence of the driving actions and the ability to recover from driving mistakes. In this paper, we develop an uncertainty-aware end-to-end trajectory generation method based on imitation learning. It can extract spatiotemporal features from the front-view camera images for scene understanding, and then generate collision-free trajectories several seconds into the future. The experimental results suggest that under various weather and lighting conditions, our network can reliably generate trajectories in different urban environments, such as turning at intersections and slowing down for collision avoidance. Furthermore, closed-loop driving tests suggest that the proposed method achieves better cross-scene/platform driving results than the state-of-the-art (SOTA) end-to-end control method, where our model can recover from off-center and off-orientation errors and capture 80% of dangerous cases with high uncertainty estimations.

Distribution of Polycyclic Aromatic Hydrocarbons in the Soil–Plant System as Affected by Motor Vehicles in Urban Environment

Distribution of Polycyclic Aromatic Hydrocarbons in the Soil–Plant System as Affected by Motor Vehicles in Urban Environment

Coarse-to-Fine Loosely-Coupled LiDAR-Inertial Odometry for Urban Positioning and Mapping

Accurate positioning and mapping are significant for autonomous systems with navigation requirements. In this paper, a coarse-to-fine loosely-coupled (LC) LiDAR-inertial odometry (LC-LIO) that could explore the complementariness of LiDAR and inertial measurement unit (IMU) was proposed for the real-time and accurate pose estimation of a ground vehicle in urban environments. Different from the existing tightly-coupled (TC) LiDAR-inertial fusion schemes which directly use all the considered ranges and inertial measurements to optimize the vehicle pose, the method proposed in this paper performs loosely-couped integrated optimization with the high-frequency motion prediction, which was produced by IMU integration based on optimized results, employed as the initial guess of LiDAR odometry to approach the optimality of LiDAR scan-to-map registration. As one of the prominent contributions, thorough studies were conducted on the performance upper bound of the TC LiDAR-inertial fusion schemes and LC ones, respectively. Furthermore, the experimental verification was performed on the proposition that the proposed pipeline can fully relax the potential of the LiDAR measurements (centimeter-level ranging accuracy) in a coarse-to-fine way without being disturbed by the unexpected IMU bias. Moreover, an adaptive covariance estimation method employed during LC optimization was proposed to explain the uncertainty of LiDAR scan-to-map registration in dynamic scenarios. Furthermore, the effectiveness of the proposed system was validated on challenging real-world datasets. Meanwhile, the process that the proposed pipelines realized the coarse-to-fine LiDAR scan-to-map registration was presented in detail. Comparing with the existing state-of-the-art TC-LIO, the focus of this study would be placed on that the proposed LC-LIO work could achieve similar or better accuracy with a reduced computational expense.

Bird Velocity Optimization as Inspiration for Unmanned Aerial Vehicles in Urban Environments

Small unmanned aerial vehicles (SUAVs) operating in urban environments must deal with complex wind flows and endurance limitations caused by current battery technology. Birds offer inspiration regarding how to fly in these environments and how to exploit complex wind flows as an energy source. On a broad scale, migrating birds adjust airspeed to minimize cost of transport (COT) in response to wind conditions, but it is unknown whether birds implement these strategies in fine-scale, complex environments. GPS backpacks were used to track 11 urban nesting gulls and found they soared extensively during daily commutes, using thermal and orographic updrafts. This paper outlines COT theory and proposes a model for optimizing airspeed for wind while maintaining flight trajectory. The gull flight paths were tested for COT adjustments, considering their flapping and soaring strategies, and it was found that the birds were able to make energy savings of 31% based on having a best glide speed when soaring that was similar to their minimum power speed when flapping. These models calculated optimum airspeeds based on wind speed and direction and could be implemented on SUAV platforms with wind sensing capabilities. This approach could significantly reduce energy requirements for SUAVs flying in urban environments.

Safety Assessment and Simulation of Autonomous Vehicle in Urban Environments

The Autonomous Vehicle (AV) industry aims to design strategic plans to ensure the safety of the developed systems before their mass deployment. Real-road testing is shown to be impractical for validating these systems as it requires many years if not decades of testing in different environmental conditions. Therefore, this method must be complemented with simulation for a feasible solution. The primary goal of this research is to develop advanced techniques in the safety validation area by using immersive simulation technologies. This study led to a simulation approach for safety evaluation of an AV shuttle, ise Auto, currently operating at the TalTech campus. First, we create a virtual environment based on the specified path on the university campus including all relevant features. The environment is created by using geospatial data which is collected by a drone, then they are converted to a 3D map applicable for the LGSVL simulator. Next, the developed shuttle 3D model is imported to the simulation environment and with the aim of the LGSVL, sensor data are provided for the Autoware perception algorithms, which is the control software of the shuttle. This system enables us to evaluate AV’s decision-making performance and safety in different situations.

Improving trajectory estimation using 3D city models and kinematic point clouds

AbstractAccurate and robust positioning of vehicles in urban environments is of high importance for autonomous driving or mobile mapping. In mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using global navigation satellite systems are targeted. This requirement is often not guaranteed in shadowed cities where global navigation satellite system signals are usually disturbed, weak or even unavailable. We propose a novel approach which incorporates prior knowledge (i.e., a 3D city model of the environment) and improves the trajectory. The recorded point cloud is matched with the semantic city model using a point‐to‐plane iterative closest point method. A pre‐classification step enables an informed sampling of appropriate matching points. Random forest is used as classifier to discriminate between facade and remaining points. Local inconsistencies are tackled by a segmentwise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The general applicability of the method implemented is demonstrated on an inner‐city data set recorded with a mobile mapping system.

High precision positioning using vehicle trajectories in urban environments (Improvement of RTK-GNSS by optimizing the initial conditions)

This study proposes a method for estimating the positions of vehicles in urban environments with high accuracy. We employ satellite positioning by GNSS for position estimation. Real-time kinematic-global navigation satellite systems (RTK-GNSS) with high precision in satellite positioning can estimate positions with centimeter-scale accuracy. However, in urban areas, the position estimation performance deteriorates owing to multipath errors. Therefore, we propose a method to improve the positioning results by increasing the robustness against multipath using vehicle trajectory. The vehicle trajectory estimates the travel route using the attitude angle and speed. Attitude angles are heading, pitching and slip angle. Trajectories can be generated with 0.5m error performance per 100m. In the proposed method, the trajectory is used as a constraint to solve the multipath of RTK-GNSS. In the evaluation test, the ratio of high-accuracy position estimation improved by up to approximately 30% compared to the conventional method. It is assumed that this method can enhance the development of self-driving cars, AGV control and SLAM technology by eliminating errors and calculating reliability.

Direction of arrival estimation of partial sound sources of vehicles with a two-microphone array

The generalized cross-correlation with phase transform (GCC-PHAT) algorithm has proved to be useful for blindly estimating the direction of arrival of compact sound sources from microphone array recordings. In applications with distributions of partial sources, such as the tires of vehicles in urban environments, the GCC-PHAT needs to be improved, otherwise the detected sound directions change values between directions of the main sources or correspond to an intermediate value between these directions. This paper presents an extension of the GCC-PHAT, based on post-processing of the output delay matrix and on image processing techniques, in order to separately identify directions of the sound produced by the front and rear tires of moving vehicles. The proposed approach can be extended to identify the tire noise directions produced by vehicles with multiple axles. The algorithm performance is analyzed using pass-by measurements of two-axle vehicles, acquired by a two-microphone array. The experiments were conducted with passenger vehicles of four distinct models, running at different speeds. The experimental results show that the proposed method is able to estimate the vehicle speed with an average error of 10.8 km/h and the vehicle wheelbase with 26 cm on average. A possible application is multiple source characterization for parametric vehicle sound synthesis in auralization.

ВЫБОР КОМПОНОВКИ ОСНОВНЫХ СИСТЕМ ТЯЖЕЛОГО МУЛЬТИКОПТЕРА С УЧЕТОМ ТРЕБОВАНИЙ АВИАЦИОННЫХ ПРАВИЛ

This article discusses the possibility of using a multicopter type aircraft as a transport vehicle in urban environments. This type of transport is the most promising in the near future, given the constantly growing number of traffic jams in large cities. The assessment of this aircraft is presented, taking into account the reliability requirements specified in the regulatory documents. The analysis of the aviation regulations requirements in terms of the aircraft systems reliability was carried out. Also the paper presents six options for the multicopter layout with a different number of propellers, taking into account the propeller control system, power supply system and control system. Other onboard systems are not considered, since their failures do not lead to an aircraft crash. An assessment of these options, taking into account the multicopter most critical onboard equipment layout according to reliability criterion by budgeting the requirements for one thrust generation channel, is given. Fault trees and failure situations that lead to a disastrous situation are also presented for each multicopter layout separately, as well as proposals for the number of independent channels for the power supply system and control system. Based on the set quantitative requirements for the occurrence of a catastrophic situation for the main on-board equipment and various layout options, taking into account the number of propellers and proposals for their architecture, power supply systems and control systems the most promising multicopter architecture options are determined. They meet the requirements for failure safety for a catastrophic situation.

A Three Dimensional MIMO Channel Model for Unmanned Aerial Vehicle in Urban Environments

Increasing the availability of Unmanned Aerial Vehicles (UAV's) platforms leads to a variety of applications for aerial exploration, surveillance, and transport. Many of these applications rely on the communication between the UAV and the ground receiver which is subjected to high mobility that may lead to restrictions on link connectivity and throughput. In order to design high throughput and efficient communication schemes for these scenarios, a deep understanding of the communication channel behavior is required, especially taking into account measurement data from flight experiments. Channel propagation in urban environments involves diffraction effects which modify the Line-of-Sight (LoS) contribution of the total received signal, especially when the receiver is located on the ground. This process leads to scenarios where Multiple-Input Multiple-Output (MIMO) signal processing can take advantage from this situation. In this context, the goal of this paper is to study the diffraction effects of the LoS component through spatial correlation metrics of the signal. To accomplish this, we propose the use of a geometric stochastic technique to model the channel behavior which lies between High Altitude Platforms (HAP) and terrestrial link communications.

Platooning of Car-Like Vehicles in Urban Environments: Longitudinal Control Considering Actuator Dynamics, Time Delays, and Limited Communication Capabilities

This brief proposes a longitudinal control framework for platooning in an urban environment. The targeted application is to redistribute vehicles involved in car-sharing systems, where only the leader vehicle is human-driven. We propose a platoon model and control law considering actuator dynamics. This control relies on a hybrid information flow topology (IFT), where the leader broadcasts its state, but each follower only measures the position of its predecessor. A consensus-based control law incorporates the effect of the network/sensor time delay and the variable velocity of the leader. Conditions for the platoon internal and string stability are given. Experiments demonstrate the efficiency of the framework in simulation and real experiments with three commercial cars.

Platooning of Car-Like Vehicles in Urban Environments: An Observer-Based Approach Considering Actuator Dynamics and Time Delays

In this paper, a distributed observer-based approach is proposed to control the longitudinal motion of car-like vehicle platoon moving in an urban environment. To the best of our knowledge, this is the first work presenting an observer-based platoon controller that combines the advantages of high traffic capacity and a minimum number of communication links. To achieve a high traffic flow, a constant-spacing policy is used. However, for that policy, to make platoon string stable, the leader information must be broadcast to all the vehicles. Therefore, we propose a control law in which the predecessor position information is acquired by a sensor-based link while a communication-based link is used to obtain the leader information. Then, an observer is designed and integrated into the control law such that the velocity information of the predecessor can be estimated without the need to communicate with the preceding vehicle. For navigation in urban environments, we present a third order platoon model represented in the curvilinear coordinates. Conditions for asymptotic stability and string stability are given considering the vehicle actuator dynamics and the induced network/sensor time delay. Finally, we provide both simulation and real-time results to validate our approach feasibility and to corroborate our theoretical findings.

Delay-Optimized V2V-Based Computation Offloading in Urban Vehicular Edge Computing and Networks

The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. In view of the delay-sensitive property of these promising applications, as well as the high expense by using infrastructures and roadside units (RSU), the task offloading among vehicles has gained enormous popularity considering its free-of-charge and timely response. In this paper, by utilizing the gathering period of vehicles in urban environment due to stopped by traffic lights or Area of Interest (AOI), a task offloading scheme merely relying on vehicle-to-vehicle (V2V) communication is proposed by fully exploring the idle resources of gathered vehicles for task execution. Through formulating the task execution as a Min-Max problem among one task and several cooperative vehicles, the task executing time is optimized with the Max-Min Fairness scheme, which is further solved by the Particle Swarm Optimization (PSO) Algorithm. Extensive simulation demonstrate that our model could well meet the delay requirement of delay-sensitive application by cooperative computing among vehicles.

Path Tracking Control for Urban Autonomous Driving

Abstract A path tracking controller for autonomous vehicles in urban environments is presented. Based on system inversion, the steering angle causing the vehicle to follow the path in absence of disturbances is calculated. Then, the lateral distance and the orientation error w.r.t. the path are compensated by a state feedback controller. Further, a decoupling of the velocity is considered in the system-inversion and the feedback controller. Therefore, ideally, the velocity does not influence path tracking and, hence, the requirements on velocity control are relaxed. To reduce the effort for parameter identification, the controller is intentionally based on a kinematic vehicle model requiring less parameters compared to an elaborated dynamic model. It is assumed that the effects of unconsidered system components, e.g., tire slip, are then compensated by the state-feedback controller. The approach is validated on a closed proving-ground in a simulated urban scenario. Herein, for driving velocities up to 14 m/s and curve radii of down to 10 m, an RMS tracking error for the lateral distance to the path of 7.2 cm was achieved. The control system will be used in TomTom’s autonomous car ‘Trillian’ that serves as a validation and research platform to evaluate high definition maps of road networks.

Future Urban Charging Solutions for Electric Vehicles

The numbers of electric vehicles (EV) will increase as many countries perceive EVs as a solution to reduce the emissions of transportation and therefore incentivize their adoption. However, the deployment of public charging infrastructure is lagging behind that of EVs, which represents a potential barrier to their wide-scale adoption. The objective of this paper is to develop a comprehensive overview of potential EV charging solutions to be deployed in urban areas. Using a micro-Delphi approach, experts from transport, energy and urban planning were consulted and identified 15 realistic options for charging electric vehicles in urban environments by 2035. The solutions range from purely technical to more service oriented. Most of these solutions already exist today, although some remain at a very early stage of deployment. The five most likely options were on-street public charging points, charging at work, fast-charging stations, using building domestic plugs and semi-fast charging in public areas. When combined with the typical mobility and residential profiles, our results show that EV drivers will most likely rely on a mix of solutions, when they have no home chargers. As such, no breakthrough or major shift is expected in charging infrastructures, rather a scale-up of existing solutions. Our analysis concludes that urban charging options will be numerous and no single solution is expected to dominate as users with different EV user profiles will charge at different times and locations.

Bidirectional Long Shot-Term Memory-Based Interactive Motion Prediction of Cut-In Vehicles in Urban Environments

This paper presents an interactive motion predictor to infer the intention of cut-in vehicles using a bidirectional long short-term memory (Bi-LSTM) module. The proposed predictor consists of three modules: maneuver recognition, trajectory prediction, and interaction. The driving data for training and validating the Bi-LSTM module were collected by sensors mounted on an autonomous vehicle (AV). In total, 3,828 trajectories of human-driven vehicles around the AV are accumulated in a global coordinate system. After postprocessing the collected trajectories, 83,188 and 35,652 data samples were used to train and validate the Bi-LSTM module, respectively. In the Bi-LSTM module, a maneuver is defined as the desired driving lane of a vehicle, which extend the behavior coverage of the proposed approach. The trajectory prediction step is based on the path-following model with a motion parameter estimator to predict the trajectories for all possible maneuvers. The interaction module considers the likelihood of each maneuver and the collision risk to determine the future trajectories of the surrounding vehicles in terms of the driving scene. The proposed predictor was evaluated in terms of its prediction accuracy and its effects on the motion planner of the AV. It has been shown that the AV benefits from the improved motion prediction of target vehicles provided by the proposed predictor with respect to enhanced safety and reduced control effort in the case of cut-in situations.

Corridor-Based Shared Autonomy for Teleoperated Driving

Abstract Given the on-going development of powerful hardware, software and algorithms for automated driving, the number of tasks that vehicles can solve autonomously steadily increases. However, fully autonomous driving in all situations is highly demanding and currently not feasible yet. It may happen that the vehicle faces situations in which the decision system is overstrained and, hence, falls back into a predefined safe state. In the future, moreover, neither control interfaces like a steering wheel and foot pedals nor a qualified driver may be available to assist the car in a blocking situation. This contribution, hence, presents a concept for teleoperated driving, i.e., a remote operation with distinct human machine interactions that explicitly addresses such highly complex driving tasks. It quickly copes with such undesired blocking situations in order to minimize the need for a both time- and cost-intensive road assistance. The concept focusses on teleoperated driving of road vehicles in urban environments. Based on the methodology of a shared autonomy, a corridor-based planning scheme is derived. The remote operation task takes advantage of a fusion of automated driving functions and human-predefined corridors. Within this specified corridor, a path planning algorithm using dual projected Newton method determines a collision-free path that the vehicle is capable to follow. Simulation results show the effectiveness of the proposed method and highlight the achieved driving safety.

Time-Dependent Hybrid-State A⁎ and Optimal Control for Autonomous Vehicles in Arbitrary and Dynamic Environments

Abstract The development of driving functions for autonomous vehicles in urban environments is still a challenging task. In comparison with driving on motorways, a wide variety of moving road users, such as pedestrians or cyclists, but also the strongly varying and sometimes very narrow road layout pose special challenges. The ability to make fast decisions about exact maneuvers and to execute them by applying sophisticated control commands is one of the key requirements for autonomous vehicles in such situations. In this context we present an algorithmic concept of three correlated methods. Its basis is a novel technique for the automated generation of a free-space polygon, providing a generic representation of the currently drivable area. We then develop a time-dependent hybrid-state A⁎ algorithm as a model-based planner for the efficient and precise computation of possible driving maneuvers in arbitrary dynamic environments. While on the one hand its results can be used as a basis for making short-term decisions, we also show their applicability as an initial guess for a subsequent trajectory optimization in order to compute applicable control signals. Finally, we provide numerical results for a variety of simulated situations demonstrating the efficiency and robustness of the proposed methods.

AUTOMATIC VEHICLE RECOGNITION FOR URBAN TRAFFIC MANAGEMENT

Abstract. Automatic vehicle recognition has an important role for many applications such as supervision, traffic management and rescue tasks. The ability of online supervision on the distribution of vehicles in urban environments prevents traffic, which in turn reduces air pollution and noise. However, this is extremely challenging due to the small size of vehicles, their different types and orientations, and the visual similarity to some other objects in very high resolution images. In this paper, an automatic vehicle recognition algorithm is proposed based on very high spatial resolution aerial images. In the first step of the proposed method, by generating the image pyramid, the candidate regions of the vehicles are recognized. Then, performing reverse pyramid, decision level fusion of the vehicle candidates and the land use/cover classification results of the original image resolution are performed in order to modify recognized vehicle regions. For evaluating the performance of the proposed method in this study, Ultracam aerial imagery with spatial resolution of 11 cm and 3 spectral bands have been used. Comparing the obtained vehicle recognition results from the proposed decision fusion algorithm with some manually selected vehicle regions confirm the accuracy of about %80. Moreover, the %78.87 and 0.71 are respectively the values for overall accuracy and Kappa coefficient of the obtained land use/cover classification map from decision fusion algorithm.