Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side by side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

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.

A Review of Motion Planning Techniques for Automated Vehicles

Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side-by-side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.

A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research.

Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

http://www.ce.unipr.it/people/bertozzi/pap/cr/viac-control2012.pdf

Autonomous vehicles control in the VisLab Intercontinental Autonomous Challenge

This paper presents an autonomous driving test held in Parma on urban roads and freeways open to regular traffic. During this test, the vehicle not only performed simple maneuvers, but it had to cope with complex driving scenarios as well, including roundabouts, junctions, pedestrian crossings, freeway junctions, and traffic lights. The test demonstrated the ability of the current technology to manage real situations and not only the well-structured and predictable ones. A comparison of milestones, challenges, and key results in autonomous driving is presented to highlight the novelty and the specific purpose of the test. The whole system is described: the vehicle; the software architecture; details about high-, medium-, and low-level control; and details about perception algorithms. A conclusion highlights the achieved results and draws possible directions for future development.

PROUD—Public Road Urban Driverless-Car Test

The presence of autonomous vehicles on public roads is becoming a reality. In the last 10 years, autonomous prototypes have been confined in controlled or isolated environments, but new traffic regulations for testing and direct automotive companies interests are moving autonomous vehicles tests on real roads. This paper presents a test on public urban roads and freeways that was held in Parma on July 12, 2013. This was the first test in open public urban roads with nobody behind the steering wheel: the vehicle had to cope with roundabouts, junctions, pedestrian crossings, freeway junctions, traffic lights, and regular traffic. The vehicle setup, the software architecture, and the route are here presented together with some results and possible future improvements.

/pdf/proud-public-road-urban-driverless-test-architecture-and-1gr865ykcg.pdf

PROUD-Public road urban driverless test: Architecture and results

This paper presents the preliminary results of VIAC, the VisLab Intercontinental Autonomous Challenge, a test of autonomous driving along an unknown route from Italy to China. It took 3 months to run the entire test; all data have been logged, including all data generated by the sensors, vehicle data, and GPS info. This huge amount of information has been packed during the trip, compressed, and transferred back to Parma for further processing. This data is now ready for a deep analysis of the various systems performance, with the aim of virtually running the whole trip multiple times with improved versions of the software. This paper discusses some preliminary figures obtained by the analysis of the data collected during the test. More information will be generated by a deeper analysis, which will take additional time, being the data about 40 terabyte in size.

/pdf/viac-an-out-of-ordinary-experiment-l3r2392752.pdf

VIAC: An out of ordinary experiment

This paper presents a method for solving the extrinsic calibration between camera and multi-layer laser scanner for outdoor multi-sensorized vehicles. The proposed method is designed for intelligent vehicles within the autonomous navigation task where usually distances between sensor and targets become relevant for safety reasons, therefore high accuracy across different measures must be kept. The calibration procedure takes advantage of triangular shapes still present in scenarios, it recovers three virtual points as target pose in the laser and camera reference frames and then compute extrinsic information of each camera sensor with respect to a laser scanner by minimizing a geometric distance in the image space. To test algorithm correctness, and accuracy a set of simulations are used reporting absolute error results and solution convergence, then tests on robustness and reliability (i.e., outliers management) are based on a wide set of datasets acquired by VIAC prototypes.

/pdf/automated-extrinsic-laser-and-camera-inter-calibration-using-9r47eugnyr.pdf

Matteo Panciroli

Papers

Autonomous vehicles control in the VisLab Intercontinental Autonomous Challenge

PROUD—Public Road Urban Driverless-Car Test

PROUD-Public road urban driverless test: Architecture and results

VIAC: An out of ordinary experiment

Automated extrinsic laser and camera inter-calibration using triangular targets