# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 9 Deep Learning for Intention Estimation  
Intent estimation is the latter part of the detection and intent estimation system (Figure 1). Avoiding incidents involving autonomous vehicles and VRUs is a critical aspect of the fully automated vehicles [14; 181; 182]. Even with success in pedestrian detection using deep networks [5], it is not enough to simply identify the pedestrian or cyclist. The autonomous vehicle must also predict if there is a chance of any harm that may befall the pedestrian/cyclist due to the autonomous vehicle action or inaction [181]. Predicting the motion and future behaviour of VRUs can aid in improving safety for autonomous vehicles and other road users, and, therefore, can be considered as a critical part of self-driving vehicles [183]. Once detection of the pedestrian/cyclist is completed, it must be considered whether the pedestrian may be in danger given their location with respect to the current distance, motion and path of the autonomous vehicle. Even when detection and localisation of the pedestrian are achieved, pose estimation and tracking must be considered as it can allow the vehicle to take actions or manoeuvres to prevent accidents if the pedestrian's or cyclist's intentions are considered to cross the path of the vehicle [184]. However, this can be difficult, especially for long-term predictions. A major cause for this is the agile nature of a human, who can very quickly change speed and direction, and may not allow the vehicle to have sufficient time to react. This limits the reliability of the prediction system as seen in [185]. Reduced accuracy in prediction could mean that the vehicle could misinterpret or not react in time to a pedestrian's or cyclist's sudden movements. In [183], minimisation of the false detections is described for improved accuracy for long-term predictions. DL has been used for intention estimation [186; 187] with promising results as will be discussed in this section. Some of the literature for intent estimation can be found in [178; 183; 188; 189; 190; 191; 192; 193; 194; 195; 196; 197; 198] which is summarised in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Object Type** & **Visible** & **Multispectral** & **Intent Estimation** \\ \hline Pedestrian & Caltech [173] & & \\ KITTI [176] & & & \\ CityScapes [177] & KAIST [106] & Daimler [178] \\ ETH [69] & & & \\ TUD-Brussels [175] & & & \\ PASCAL-VOC [179] & & & \\ \hline Cyclist & Tsinghua-Daimler [48] & FLIR [180] & \\ KITTI [176] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Datasets for Pedestrians and Cyclists. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Study** & **Approach 4 \& Overview** & **Technique 5 \& Performance** & **Evaluation 6** \\  & \& Sensor & & & \\ \hline \hline \multirow{9}{*}{[178]} & \multirow{9}{*}{ \begin{tabular}{} \end{tabular}  } & \multirow{9}{*}{ \begin{tabular}{} \end{tabular}  } & \multicolumn{5}{c}{The models were applied for four pedestrian motions;} \\  & & & crossing, stopping, bending and starting. & Image sequences were recorded using a stereo camera system. \\  & & & position measurements were obtained using a vision-based pedestrian & The dataset contained 12,485 images \\  & & & & and multiple models had & \\  & & & using & very similar performance pedestrians. A \\  & & & single/ & in terms of position estimation. This could be & \\  & & & & \\  & & & models. & due to a high sampling rate and low measurement error. However, for path prediction, the IMM & \\  & & & & \\  & & models with EKF. & & outperformed the single model-based approach and could improve position estimation of up to 30 cm. & \\ \hline \hline \multirow{9}{*}{[199]} & \multirow{9}{*}{ \begin{tabular}{} \end{tabular}  } & \multirow{9}{*}{ \begin{tabular}{} \end{tabular}  } & \multicolumn{5}{c}{Intention prediction up} \\  & & & to 1s ahead of time using this technique.