Humans drive in a holistic fashion which entails, in particular, understanding dynamic road events and their evolution. Injecting these capabilities in autonomous vehicles can thus take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. ROAD comprises videos originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event. We benchmark various detection tasks, proposing as a baseline a new incremental algorithm for online road event awareness termed 3D-RetinaNet. We also report the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as that of the winners of the ICCV2021 ROAD challenge, which highlight the challenges faced by situation awareness in autonomous driving. ROAD is designed to allow scholars to investigate exciting tasks such as complex (road) activity detection, future event anticipation and continual learning. The dataset is available at https://github.com/gurkirt/road-dataset; the baseline can be found at https://github.com/gurkirt/3D-RetinaNet.
The rising popularity of autonomous vehicles has led to the development of driverless racing cars, where the competitive nature of motorsport has the potential to drive innovations in autonomous vehicle technology. The challenge of racing requires the sensors, object detection and vehicle control systems to work together at the highest possible speed and computational efficiency. This paper describes an autonomous driving system for a self-driving racing vehicle application using a modest sensor suite coupled with accessible processing hardware, with an object detection system capable of a frame rate of 25fps, and a mean average precision of 92%. A modelling tool is developed in open-source software for real-time dynamic simulation of the autonomous vehicle and associated sensors, which is fully interchangeable with the real vehicle. The simulator provides performance metrics, which enables accelerated and enhanced quantitative analysis, tuning and optimisation of the autonomous control system algorithms. A design study demonstrates the ability of the simulation to assist in control system parameter tuningresulting in a 12% reduction in lap time, and an average velocity of 25 km/h -indicating the value of using simulation for the optimisation of multiple parameters in the autonomous control system.
Autonomous vehicles rely heavily upon their perception subsystems to 'see' the environment in which they operate. Unfortunately, the effect of varying weather conditions presents a significant challenge to object detection algorithms, and thus it is imperative to test the vehicle extensively in all conditions which it may experience. However, unpredictable weather can make real-world testing in adverse conditions an expensive and time consuming task requiring access to specialist facilities, and weatherproofing of sensitive electronics. Simulation provides an alternative to real world testing, with some studies developing increasingly visually realistic representations of the real world on powerful compute hardware. Given that subsequent subsystems in the autonomous vehicle pipeline are unaware of the visual realism of the simulation, when developing modules downstream of perception the appearance is of little consequence -rather it is how the perception system performs in the prevailing weather condition that is important. This study explores the potential of using a simple, lightweight image augmentation system in an autonomous racing vehicle -focusing not on visual accuracy, but rather the effect upon perception system performance. With minimal adjustment, the prototype system developed in this study can replicate the effects of both water droplets on the camera lens, and fading light conditions. The system introduces a latency of less than 8 ms using compute hardware that is well suited to being carried in the vehicle -rendering it ideally suited to real-time implementation that can be run during experiments in simulation, and augmented reality testing in the real world.
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