Obstacle Avoidance, Path Planning and Control for Autonomous Vehicles

Laghmara, Hind; Boudali, Mohamed-Taha; Laurain, Thomas; Ledy, Jonathan; Orjuela, Rodolfo; Lauffenburger, Jean-Philippe; Basset, Michel

doi:10.1109/ivs.2019.8814173

Cited by 30 publications

(18 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For an automated driving development, many fields are explored particularly the perception, the path planning and the control guidance. In fact, these three fields are the needed pillars to build a driverless hierarchical architecture [10], [11] as shown in Fig. 1.…”

Section: A Related Workmentioning

confidence: 99%

An Emergency Hierarchical Guidance Control Strategy for Autonomous Vehicles

Khelladi

Boudali

Orjuela

et al. 2022

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

This paper introduces a vehicle guidance control architecture capable of autonomously resolving emergency situations in presence of a steering system failure. The proposed architecture is based on a hierarchical approach composed of three levels, namely the reference generation level, the guidance control level, and the control allocation level. The reference generation first delivers a normal or an emergency trajectory reference and the according speed profile depending on the information sent by the fault detection and isolation (FDI) supervisor. The emergency trajectory allows the vehicle to reach safely the emergency stop strip. The reference trajectory and speed profile tracking is obtained by decoupling the longitudinal and lateral control at a guidance level. Respective errors are subsequently converted into steering and braking/accelerating torques commands. The lowest level of the hierarchy, namely the control allocation, is meant to allocate the control commands to the multiple available actuators according to the FDI information. For example, promoting differential braking when a steering system failure is detected, guaranteeing acceptable tracking performance both in longitudinal and lateral directions.

show abstract

Section: A Related Workmentioning

confidence: 99%

An Emergency Hierarchical Guidance Control Strategy for Autonomous Vehicles

Khelladi

Boudali

Orjuela

et al. 2022

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Path Planning is an important subtask of autonomous navigation and is generally termed as a problem of searching for a path which an autonomous system has to follow in a described environment and requires the vehicle to go in the direction closest to the goal, and, generally, the map of the area is already known [220][221][222][223]. Path planning when used in conjunction with techniques of obstacle avoidance [223] gives a more robust deployment of the path planner module by enabling the system to avoid hazardous collision objects, no-go zones, and negative objects like potholes and similar objects.…”

Section: Path Planningmentioning

confidence: 99%

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

Kolar

Benavidez

Jamshidi

2020

Sensors

View full text Add to dashboard Cite

This paper focuses on data fusion, which is fundamental to one of the most important modules in any autonomous system: perception. Over the past decade, there has been a surge in the usage of smart/autonomous mobility systems. Such systems can be used in various areas of life like safe mobility for the disabled, senior citizens, and so on and are dependent on accurate sensor information in order to function optimally. This information may be from a single sensor or a suite of sensors with the same or different modalities. We review various types of sensors, their data, and the need for fusion of the data with each other to output the best data for the task at hand, which in this case is autonomous navigation. In order to obtain such accurate data, we need to have optimal technology to read the sensor data, process the data, eliminate or at least reduce the noise and then use the data for the required tasks. We present a survey of the current data processing techniques that implement data fusion using different sensors like LiDAR that use light scan technology, stereo/depth cameras, Red Green Blue monocular (RGB) and Time-of-flight (TOF) cameras that use optical technology and review the efficiency of using fused data from multiple sensors rather than a single sensor in autonomous navigation tasks like mapping, obstacle detection, and avoidance or localization. This survey will provide sensor information to researchers who intend to accomplish the task of motion control of a robot and detail the use of LiDAR and cameras to accomplish robot navigation.

show abstract

“…However, as their schemes rely only on ego-vehicle's information, it has limitations on collisions predictions. In [30,31], collision avoidance schemes based on occupancy grid for autonomous vehicle were considered. The state of cells in occupancy grid is determined by the DS that combines the Light Detection And Ranging (LiDAR) data.…”

Section: Related Workmentioning

confidence: 99%

“…Let us consider methods to obtain object type data. Recently, LiDAR-based autonomous vehicle systems have been investigated [31,32,61]. However, due to its high cost [62], most of commercial vehicles such as Tesla and Mercedes-Benz adopt the computer vision as primary object detection rather than LiDAR [45].…”

Section: Object Sensingmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Inter-Vehicle Data Fusion Based on a New Metric for Evidence Distance in Autonomous Vehicle Systems

2020

View full text Add to dashboard Cite

Safety is a major concern for autonomous vehicle driving. The autonomous vehicles relying solely on ego-vehicle sensors have limitations in dealing with collisions. The risk can be reduced by communicating with other vehicles sharing sensed information. In this paper, we study a real-time multisource data fusion scheme based on Dempster–Shafer theory of evidence (DS) through cooperative vehicle-to-vehicle (V2V) communications. The classical DS can produce erroneous outputs when confidences are highly conflicting. In particular, a false negative error on object detection may lead to serious accidents. We propose a novel metric to measure distance between confidences on the collected data taking the asymmetry in the importance of sensed events into account. Based on the proposed distance, we consider the weighted DS combination in order to fuse multiple confidences. The proposed scheme is simple, and enables the vehicles to combine confidences in real-time. From real-world experiments, we show that it is feasible to lower the false negative rate by the proposed data fusion scheme, and to avoid accidents using the proposed collision warning system.

show abstract

Obstacle Avoidance, Path Planning and Control for Autonomous Vehicles

Cited by 30 publications

References 16 publications

An Emergency Hierarchical Guidance Control Strategy for Autonomous Vehicles

An Emergency Hierarchical Guidance Control Strategy for Autonomous Vehicles

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

Real-Time Inter-Vehicle Data Fusion Based on a New Metric for Evidence Distance in Autonomous Vehicle Systems

Contact Info

Product

Resources

About