CASPNet++: Joint Multi-Agent Motion Prediction

The prediction of road users' future motion is a critical task in supporting advanced driver-assistance systems (ADAS). It plays an even more crucial role for autonomous driving (AD) in enabling the planning and execution of safe driving maneuvers. Based on our previous work, Context-Aware Scene Prediction Network (CASPNet), an improved system, CASPNet++, is proposed. In this work, we focus on further enhancing the interaction modeling and scene understanding to support the joint prediction of all road users in a scene using spatiotemporal grids to model future occupancy. Moreover, an instance-based output head is introduced to provide multi-modal trajectories for agents of interest. In extensive quantitative and qualitative analysis, we demonstrate the scalability of CASPNet++ in utilizing and fusing diverse environmental input sources such as HD maps, Radar detection, and Lidar segmentation. Tested on the urban-focused prediction dataset nuScenes, CASPNet++ reaches state-of-the-art performance. The model has been deployed in a testing vehicle, running in real-time with moderate computational resources.Comment: 8 pages, 6 figure

Influence of Lane Width on Semi-Autonomous Vehicle Performance

[EN] In the medium-term, the number of semi-autonomous vehicles is expected to rise significantly. These changes in vehicle capabilities make it necessary to analyze their interaction with road infrastructure, which has been developed for human-driven vehicles. Current systems use artificial vision, recording the oncoming road and using the center and edgeline road markings to automatically facilitate keeping the vehicle within the lane. In addition to alignment and road markings, lane width has emerged as one of the geometric parameters that might cause disengagement and therefore must be assessed. The objective of this research was to study the impact of lane width on semi-autonomous vehicle performance. The automatic lateral control of this type of vehicle was tested along 81 lanes of an urban arterial comprising diverse widths. Results showed that the semi-autonomous system tended to fail on narrow lanes. There was a maximum width below which human control was always required-referred to as the human lane width-measuring 2.5 m. A minimum width above which automatic control was always possible-the automatic lane width-was established to be 2.75 m. Finally, a lane width of 2.72 m was found to have the same probability of automatic and human lateral control, namely the critical lane width. Following a similar methodology, these parameters could be determined for other vehicles, enhancing the interaction between autonomous vehicles and road infrastructure and thus supporting rapid deployment of autonomous technology without compromising safety.García García, A.; Camacho-Torregrosa, FJ. (2020). Influence of Lane Width on Semi-Autonomous Vehicle Performance. Transportation Research Record. 2674(9):279-286. https://doi.org/10.1177/0361198120928351S27928626749Lu, Z., Zhang, B., Feldhütter, A., Happee, R., Martens, M., & De Winter, J. C. F. (2019). Beyond mere take-over requests: The effects of monitoring requests on driver attention, take-over performance, and acceptance. Transportation Research Part F: Traffic Psychology and Behaviour, 63, 22-37. doi:10.1016/j.trf.2019.03.018Dogan, E., Rahal, M.-C., Deborne, R., Delhomme, P., Kemeny, A., & Perrin, J. (2017). Transition of control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement. Transportation Research Part F: Traffic Psychology and Behaviour, 46, 205-215. doi:10.1016/j.trf.2017.01.012Shen, S., & Neyens, D. M. (2017). Assessing drivers’ response during automated driver support system failures with non-driving tasks. Journal of Safety Research, 61, 149-155. doi:10.1016/j.jsr.2017.02.009Du, X., & Tan, K. K. (2016). Comprehensive and Practical Vision System for Self-Driving Vehicle Lane-Level Localization. IEEE Transactions on Image Processing, 25(5), 2075-2088. doi:10.1109/tip.2016.2539683Du, X., & Tan, K. K. (2015). Vision-based approach towards lane line detection and vehicle localization. Machine Vision and Applications, 27(2), 175-191. doi:10.1007/s00138-015-0735-5Favarò, F., Eurich, S., & Nader, N. (2018). Autonomous vehicles’ disengagements: Trends, triggers, and regulatory limitations. Accident Analysis & Prevention, 110, 136-148. doi:10.1016/j.aap.2017.11.00

Agile Autonomous Driving using End-to-End Deep Imitation Learning

We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost sensors. By imitating a model predictive controller equipped with advanced sensors, we train a deep neural network control policy to map raw, high-dimensional observations to continuous steering and throttle commands. Compared with recent approaches to similar tasks, our method requires neither state estimation nor on-the-fly planning to navigate the vehicle. Our approach relies on, and experimentally validates, recent imitation learning theory. Empirically, we show that policies trained with online imitation learning overcome well-known challenges related to covariate shift and generalize better than policies trained with batch imitation learning. Built on these insights, our autonomous driving system demonstrates successful high-speed off-road driving, matching the state-of-the-art performance.Comment: 13 pages, Robotics: Science and Systems (RSS) 201