Control of connected and automated vehicles: State of the art and future challenges

Self-driving vehicles will soon be a reality, as main automotive companies have announced that they will sell their driving automation modes in the 2020s. This technology raises relevant controversies, especially with recent deadly accidents. Nevertheless, autonomous vehicles are still popular and attractive thanks to the improvement they represent to people’s way of life (safer and quicker transit, more accessible, comfortable, convenient, efficient, and environment-friendly). This paper presents a review of motion planning techniques over the last decade with a focus on highway planning. In the context of this article, motion planning denotes path generation and decision making. Highway situations limit the problem to high speed and small curvature roads, with specific driver rules, under a constrained environment framework. Lane change, obstacle avoidance, car following, and merging are the situations addressed in this paper. After a brief introduction to the context of autonomous ground vehicles, the detailed conditions for motion planning are described. The main algorithms in motion planning, their features, and their applications to highway driving are reviewed, along with current and future challenges and open issues.

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A Review of Motion Planning for Highway Autonomous Driving

This paper presents the first study on scheduling for cooperative data dissemination in a hybrid infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communication environment. We formulate the novel problem of cooperative data scheduling (CDS). Each vehicle informs the road-side unit (RSU) the list of its current neighboring vehicles and the identifiers of the retrieved and newly requested data. The RSU then selects sender and receiver vehicles and corresponding data for V2V communication, while it simultaneously broadcasts a data item to vehicles that are instructed to tune into the I2V channel. The goal is to maximize the number of vehicles that retrieve their requested data. We prove that CDS is NP-hard by constructing a polynomial-time reduction from the Maximum Weighted Independent Set (MWIS) problem. Scheduling decisions are made by transforming CDS to MWIS and using a greedy method to approximately solve MWIS. We build a simulation model based on realistic traffic and communication characteristics and demonstrate the superiority and scalability of the proposed solution. The proposed model and solution, which are based on the centralized scheduler at the RSU, represent the first known vehicular ad hoc network (VANET) implementation of software defined network (SDN) concept.

Cooperative data scheduling in hybrid vehicular ad hoc networks: VANET as a software defined network

A computerized system mountable on a vehicle operable to detect an object by processing first image frames from a first camera and second image frames from a second camera. A first range is determined to said detected object using the first image frames. An image location is projected of the detected object in the first image frames onto an image location in the second image frames. A second range is determined to the detected object based on both the first and second image frames. The detected object is tracked in both the first and second image frames When the detected object leaves a field of view of the first camera, a third range is determined responsive to the second range and the second image frames.

Fusion of far infrared and visible images in enhanced obstacle detection in automotive applications

Supply chain digitisation trends: An integration of knowledge management

The vehicle-following concept has been widely used in several intelligent-vehicle applications. Adaptive cruise control systems, platooning systems, and systems for stop-and-go traffic employ this concept: The ego vehicle follows a leader vehicle at a certain distance. The vehicle-following concept comes to its limitations when obstacles interfere with the path between the ego vehicle and the leader vehicle. We call such situations dynamic driving situations. This paper introduces a planning and decision component to generalize vehicle following to situations with nonautomated interfering vehicles in mixed traffic. As a demonstrator, we employ a car that is able to navigate autonomously through regular traffic that is longitudinally and laterally guided by actuators controlled by a computer. This paper focuses on and limits itself to lateral control for collision avoidance. Previously, this autonomous-driving capability was purely based on the vehicle-following concept using vision. The path of the leader vehicle was tracked. To extend this capability to dynamic driving situations, a dynamic path-planning component is introduced. Several driving situations are identified that necessitate responses to more than the leader vehicle. We borrow an idea from robotics to solve the problem. Treat the path of the leader vehicle as an elastic band that is subjected to repelling forces of obstacles in the surroundings. This elastic-band framework offers the necessary features to cover dynamic driving situations. Simulation results show the power of this approach. Real-world results obtained with our demonstrator validate the simulation results

Collision Avoidance for Vehicle-Following Systems

A crucial task for steering an autonomous vehicle along a safe path in a car following scenario is the lateral control. The sensory input of such a lateral control are the position coordinates of the leader vehicle. The following problem occurs: due to the distance between the leader vehicle and the autonomous ego-vehicle, the lateral control has to interpolate a trajectory between the two vehicles. Using as a trajectory either a straight line or a curve of constant curvature causes the ego-vehicle to deviate from the leader vehicle's trajectory. Given a system delivering 3D points of the leader vehicle with time lags, one has a handle to reconstruct the leader vehicle's trajectory. In addition, one has to compensate the motion of the ego-vehicle by using its motion parameters. Once this transformation is performed, the position coordinates of the leader vehicle are available in a coordinate system at rest. Knowing the position of the ego-vehicle in that coordinate system, one can select the trajectory point of the leader vehicle that is closest to the ego-vehicle as input to the lateral controller. This simple approach increases significantly the precision of car following systems. The algorithm is applied successfully to an autonomous car for platooning at small velocities.

A trajectory-based approach for the lateral control of car following systems

Our main objective in the paper is to perform a cartography of a road scene into a reference frame at rest, where 3D measurements delivered by on-board sensors serve as input. The main sensors of our autonomous vehicle are two CCD cameras. Their pictures are combined using stereopsis to generate 3D data. We need dead reckoning to properly associate 3D data among the frames. This necessitates us to obtain a precise ego-motion estimation. Dead reckoning using only standard vehicle odometry (velocity and steering angle) can cause non-negligible errors, especially in situations where side slip or skidding occurs. We use stationary points in the scene to support the determination of our ego-motion. Two types of stationary objects are used: Firstly, stationary vertical landmarks such as traffic signs are used to compensate errors in our localization prediction. Secondly, lane markings measured in consecutive frames are used to compensate orientation errors. Preliminary results show that dead reckoning using stationary objects can vastly improve self-localization.

Dead reckoning and cartography using stereo vision for an autonomous car

The vehicle-following principle comes to its limitations when obstacles interfere with the path between the ego-vehicle and the leader vehicle. This work introduces a planning and decision component to generalize vehicle following to situations dealing with non-automated interfering vehicles in mixed traffic. We focus on lateral control for collision avoidance. Previously, this autonomous driving capability was purely based on the vehicle-following principle. To extend this capability to dynamic driving situations, a dynamic path planning component is introduced. Several driving situations are identified that necessitate responses to more than the leader vehicle. We borrow an idea from robotics to solve the problem: treat the path of the leader vehicle as an elastic band that is subjected to repelling forces of obstacles in the surroundings. This elastic band framework offers the necessary features to cover dynamic driving situations. Simulation results show the effectiveness of this approach. Real-world results obtained with our demonstrator validate the simulation results.

Elastic bands to enhance vehicle following

Our main objective in this paper is to perform a cartography of a road scene into a reference frame at rest, where 3D measurements delivered by on-board sensors serve as input. The main sensors of our autonomous vehicle are two CCD cameras. Their pictures are combined using stereopsis to generate 3D data. We need dead reckoning to properly associate 3D data among the frames. This necessitates us to obtain a precise ego-motion estimation. Dead reckoning using only standard vehicle odometry can cause nonnegligible errors. We use stationary points in the scene to support the determination of our ego-motion. Two types of stationary objects are used: vertical landmarks such as traffic signs and lane markings are used to compensate positioning errors. Preliminary results show that cartography as proposed in this paper is beneficial to detect stationary objects but needs further work for fast moving objects.

F.J. Stein

Papers

Collision Avoidance for Vehicle-Following Systems

A trajectory-based approach for the lateral control of car following systems

Dead reckoning and cartography using stereo vision for an autonomous car

Elastic bands to enhance vehicle following

Cartography and dead reckoning using stereo vision for an autonomous car