1,192 research outputs found
Deep Predictive Models for Collision Risk Assessment in Autonomous Driving
In this paper, we investigate a predictive approach for collision risk
assessment in autonomous and assisted driving. A deep predictive model is
trained to anticipate imminent accidents from traditional video streams. In
particular, the model learns to identify cues in RGB images that are predictive
of hazardous upcoming situations. In contrast to previous work, our approach
incorporates (a) temporal information during decision making, (b) multi-modal
information about the environment, as well as the proprioceptive state and
steering actions of the controlled vehicle, and (c) information about the
uncertainty inherent to the task. To this end, we discuss Deep Predictive
Models and present an implementation using a Bayesian Convolutional LSTM.
Experiments in a simple simulation environment show that the approach can learn
to predict impending accidents with reasonable accuracy, especially when
multiple cameras are used as input sources.Comment: 8 pages, 4 figure
FIR-based Future Trajectory Prediction in Nighttime Autonomous Driving
The performance of the current collision avoidance systems in Autonomous
Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) can be drastically
affected by low light and adverse weather conditions. Collisions with large
animals such as deer in low light cause significant cost and damage every year.
In this paper, we propose the first AI-based method for future trajectory
prediction of large animals and mitigating the risk of collision with them in
low light. In order to minimize false collision warnings, in our multi-step
framework, first, the large animal is accurately detected and a preliminary
risk level is predicted for it and low-risk animals are discarded. In the next
stage, a multi-stream CONV-LSTM-based encoder-decoder framework is designed to
predict the future trajectory of the potentially high-risk animals. The
proposed model uses camera motion prediction as well as the local and global
context of the scene to generate accurate predictions. Furthermore, this paper
introduces a new dataset of FIR videos for large animal detection and risk
estimation in real nighttime driving scenarios. Our experiments show promising
results of the proposed framework in adverse conditions. Our code is available
online.Comment: Conference: IEEE Intelligent Vehicles 2023 (IEEE IV 2023
A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles
This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed
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