Autonomous Driving

Using computing to make driving safer.

DOMAINS | autonomous-driving | safety-critical-systems | vehicle-communication

PROJECTS | driver-behaviour-learning

METHODS | deep-learning | lstm

WEBPAGE: | /news/2021-10-14-AutolineInterview/

Making self-driving cars is one of the great AI challenges of the 21st Century and it involves many different parts. The goal is not merely to make fully autonomous driving cars so that humans never need to drive cars again. In fact, there are many forms of automation to every aspect of driving and coordination of vehicles on the road that can be considered.

In my lab we have done work on a few focussed topics in this area:

Multi-Vehicle Communication - In a coordinated, multi-vehicle scenario such as a convoy or fleet or autonomous cars, it is important for the autonomous cars to communicate efficiently and reliably. In this topic we have looked at some ways to do this using Deep Neural Networks.
Driver Behaviour Learning - In this line of research we look at how humans drive and try to learn models of that which can be predictive with a good level of accuracy. If autonomous vehicles drive in ways similar to, although hopefully safer than, humans, then they can more easily be integrated into the existing roads and traffic.

Our Papers on Autonomous Driving

patent

Multi-Level Collaborative Control System With Dual Neural Network Planning For Autonomous Vehicle Control In A Noisy Environment

Du, Zhiyuan, Lull, Joseph, Malhan, Rajesh, Ganapathi Subramanian, Sriram, Bhalla, Sushrut, Sambee, Jaspreet, Crowley, Mark, Fischmeister, Sebastian, Shin, Donghyun, Melek, William, Fidan, Baris, Woo, Ami, and Sahoo, Bismaya

2020

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A RLP system for a host vehicle includes a memory and levels. The memory stores a RLP algorithm, which is a multi-agent collaborative DQN with PER algorithm. A first level includes a data processing module that provides sensor data, object location data, and state information of the host vehicle and other vehicles. A second level includes a coordinate location module that, based on the sensor data, the object location data, the state information, and a refined policy provided by the third level, generates an updated policy and a set of future coordinate locations implemented via the first level. A third level includes evaluation and target neural networks and a processor that executes instructions of the RLP algorithm for collaborative action planning between the host and other vehicles based on outputs of the evaluation and target networks and to generate the refined policy based on reward values associated with events.
Deep Multi Agent Reinforcement Learning for Autonomous Driving

Bhalla, Sushrut, Ganapathi Subramanian, Sriram, and Crowley, Mark

In Canadian Conference on Artificial Intelligence 2020

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Learning Multi-Agent Communication with Reinforcement Learning

Bhalla, Sushrut, Ganapathi Subramanian, Sriram, and Crowley, Mark

In Conference on Reinforcement Learning and Decision Making (RLDM-19) 2019
Training Cooperative Agents for Multi-Agent Reinforcement Learning

Bhalla, Sushrut, Ganapathi Subramanian, Sriram, and Crowley, Mark

In Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019) 2019
Integration of Roadside Camera Images and Weather Data for monitoring Winter Road Surface Conditions

Carrillo, Juan, and Crowley, Mark

In Canadian Association of Road Safety Professionals (CARSP) Conference 2019

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Background/Context: During the Winter season, real-time monitoring of road surface conditions is critical for the safety of drivers and road maintenance operations. Previous research has evaluated the potential of image classification methods for detecting road snow coverage by processing images from roadside cameras installed in RWIS (Road Weather Information System) stations. However, it is a challenging task due to limitations such as image resolution, camera angle, and illumination. Two common approaches to improve the accuracy of image classification methods are: adding more input features to the model and increasing the number of samples in the training dataset. Additional input features can be weather variables and more sample images can be added by including other roadside cameras. Although RWIS stations are equipped with both cameras and weather measurement instruments, they are only a subset of the total number of roadside cameras installed across transportation networks, most of which do not have weather measurement instruments. Thus, improvements in use of image data could benefit from additional data sources. Aims/Objectives: The first objective of this study is to complete an exploratory data analysis over three data sources in Ontario: RWIS stations, all the other MTO (Ministry of Transportation of Ontario) roadside cameras, and Environment Canada weather stations. The second objective is to determine the feasibility of integrating these three datasets into a more extensive and richer dataset with weather variables as additional features and other MTO roadside cameras as additional sources of images. Methods/Targets: First, we quantify the advantage of adding other MTO roadside cameras using spatial statistics, the number of monitored roads, and the coverage of ecoregions with different climate regimes. We then analyze experimental variograms from the literature and determine the feasibility of using Environment Canada stations and RWIS stations to interpolate weather variables for all the other MTO roadside cameras without weather instruments. Results/Activities: By adding all other MTO cameras as image data sources, the total number of cameras in the dataset increases from 139 to 578 across Ontario. The average distance to the nearest camera decreases from 38.4km to 9.4km, and the number of monitored roads increases approximately four times. Additionally, six times more cameras are available in the four most populated ecoregions in Ontario. The experimental variograms show that it is feasible to interpolate weather variables with reasonable accuracy. Moreover, observations in the three datasets are collected with similar frequency, which facilitates our data integration approach. Discussion/Deliverables: Integrating these three datasets is feasible and can benefit the design and development of automated image classification methods for monitoring road snow coverage. We do not consider data from pavement-embedded sensors, an additional line of research may explore the integration of this data. Our approach can provide actionable insights which can be used to more selectively perform manual patrolling to better identify road surface conditions. Conclusions: Our initial results are promising and demonstrate that additional, image only datasets can be added to road monitoring data by using existing multimodal sensors as ground truth, which will lead to greater performance on the future image classification tasks.
Decision Assist for Self-Driving Cars

Ganapathi Subramanian, Sriram, Sambee, Jaspreet Singh, Ghojogh, Benyamin, and Crowley, Mark

In Canadian Conference on Artificial Intelligence 2018

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Research into self-driving cars has grown enormously in the last decade primarily due to the advances in the fields of machine intelligence and image processing. An under-appreciated aspect of self-driving cars is actively avoiding high traffic zones, low visibility zones, and routes with rough weather conditions by learning different conditions and making decisions based on trained experiences. This paper addresses this challenge by introducing a novel hierarchical structure for dynamic path planning and experiential learning for vehicles. A multistage system is proposed for detecting and compensating for weather, lighting, and traffic conditions as well as a novel adaptive path planning algorithm named Checked State A3C. This algorithm improves upon the existing A3C Reinforcement Learning (RL) algorithm by adding state memory which provides the ability to learn an adaptive model of the best decisions to take from experience. \textcopyright Springer International Publishing AG, part of Springer Nature 2018.