For an autonomous vehicle to operate safely and effectively, an accurate and robust localisation system is essential. While there are a variety of vehicle localisation techniques in literature, there is a lack of effort in comparing these techniques and identifying their potentials and limitations for autonomous vehicle applications. Hence, this paper evaluates the state-of-the-art vehicle localisation techniques and investigates their applicability on autonomous vehicles. The analysis starts with discussing the techniques which merely use the information obtained from on-board vehicle sensors. It is shown that although some techniques can achieve the accuracy required for autonomous driving but suffer from the high cost of the sensors and also sensor performance limitations in different driving scenarios (e.g. cornering, intersections) and different environmental conditions (e.g. darkness, snow). The paper continues the analysis with considering the techniques which benefit from off-board information obtained from V2X communication channels, in addition to vehicle sensory information. The analysis shows that augmenting off-board information to sensory information has potential to design low-cost localisation systems with high accuracy and robustness however their performance depends on penetration rate of nearby connected vehicles or infrastructure and the quality of network service.
Reinforcement learning has been used widely for autonomous longitudinal control algorithms. However, many existing algorithms suffer from sample inefficiency in reinforcement learning as well as the jerky driving behaviour of the learned systems. In this paper, we propose a reinforcement learning algorithm and a training framework to address these two disadvantages of previous algorithms proposed in this field.
The proposed system uses an Advantage Actor Critic (A2C) learning system with recurrent layers to introduce temporal context within the network. This allows the learned system to evaluate continuous control actions based on previous states and actions in addition to current states. Moreover, slow training of the algorithm caused by its sample inefficiency is addressed by utilising another neural network to approximate the vehicle dynamics. Using a neural network as a proxy for the simulator has significant benefit to training as it reduces the requirement for reinforcement learning to query the simulation (which is a major bottleneck) in learning and as both reinforcement learning network and proxy network can be deployed on the same GPU, learning speed is considerably improved. Simulation results from testing in IPG CarMaker show the effectiveness of our recurrent A2C algorithm, compared to an A2C without recurrent layers.
Deep learning is a promising class of techniques for controlling
an autonomous vehicle. However, functional safety validation is seen
as a critical issue for these systems due to the lack of transparency in
deep neural networks and the safety-critical nature of autonomous vehicles.
The black box nature of deep neural networks limits the effectiveness
of traditional verification and validation methods. In this paper, we propose
two software safety cages, which aim to limit the control action
of the neural network to a safe operational envelope. The safety cages
impose limits on the control action during critical scenarios, which if
breached, change the control action to a more conservative value. This
has the benefit that the behaviour of the safety cages is interpretable,
and therefore traditional functional safety validation techniques can be
applied. The work here presents a deep neural network trained for longitudinal
vehicle control, with safety cages designed to prevent forward
collisions. Simulated testing in critical scenarios shows the effectiveness
of the safety cages in preventing forward collisions whilst under normal
highway driving unnecessary interruptions are eliminated, and the deep
learning control policy is able to perform unhindered. Interventions by
the safety cages are also used to re-train the network, resulting in a more
robust control policy.
Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.